autots.models package¶
Submodules¶
autots.models.base module¶
Base model information
@author: Colin
-
class
autots.models.base.
ModelObject
(name: str = 'Uninitiated Model Name', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, fit_runtime=datetime.timedelta(0), holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, n_jobs: int = -1)¶ Bases:
object
Generic class for holding forecasting models.
- Models should all have methods:
.fit(df, future_regressor = []) (taking a DataFrame with DatetimeIndex and n columns of n timeseries) .predict(forecast_length = int, future_regressor = [], just_point_forecast = False) .get_new_params() - return a dictionary of weighted random selected parameters
- Parameters
name (str) – Model Name
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
n_jobs (int) – used by some models that parallelize to multiple cores
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basic_profile
(df)¶ Capture basic training details.
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create_forecast_index
(forecast_length: int)¶ Generate a pd.DatetimeIndex appropriate for a new forecast.
Warning
Requires ModelObject.basic_profile() being called as part of .fit()
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get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
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get_params
()¶ Return dict of current parameters.
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class
autots.models.base.
PredictionObject
(model_name: str = 'Uninitiated', forecast_length: int = 0, forecast_index=nan, forecast_columns=nan, lower_forecast=nan, forecast=nan, upper_forecast=nan, prediction_interval: float = 0.9, predict_runtime=datetime.timedelta(0), fit_runtime=datetime.timedelta(0), model_parameters={}, transformation_parameters={}, transformation_runtime=datetime.timedelta(0))¶ Bases:
object
Generic class for holding forecast information.
-
model_name
¶
-
model_parameters
¶
-
transformation_parameters
¶
-
forecast
¶
-
upper_forecast
¶
-
lower_forecast
¶
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long_form_results
()¶ return complete results in long form
-
total_runtime
()¶ return runtime for all model components in seconds
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plot
()¶
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long_form_results
(id_name='SeriesID', value_name='Value', interval_name='PredictionInterval', update_datetime_name=None) Export forecasts (including upper and lower) as single ‘long’ format output
- Parameters
id_name (str) – name of column containing ids
value_name (str) – name of column containing numeric values
interval_name (str) – name of column telling you what is upper/lower
update_datetime_name (str) – if not None, adds column with current timestamp and this name
- Returns
pd.DataFrame
-
plot
(df_wide=None, series: str = None, ax=None, remove_zeroes: bool = False, start_date: str = None, **kwargs) Generate an example plot of one series.
- Parameters
df_wide (str) – historic data for plotting actuals
series (str) – column name of series to plot. Random if None.
ax – matplotlib axes to pass through to pd.plot()
remove_zeroes (bool) – if True, don’t plot any zeroes
start_date (str) – Y-m-d string to remove all data before
passed to pd.DataFrame.plot() (**kwargs) –
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total_runtime
() Combine runtimes.
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autots.models.basics module¶
Naives and Others Requiring No Additional Packages Beyond Numpy and Pandas
-
class
autots.models.basics.
AverageValueNaive
(name: str = 'AverageValueNaive', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, method: str = 'Median', **kwargs)¶ Bases:
autots.models.base.ModelObject
Naive forecasting predicting a dataframe of the series’ median values
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
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fit
(df, future_regressor=None)¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
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get_new_params
(method: str = 'random')¶ Returns dict of new parameters for parameter tuning
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=None, just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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class
autots.models.basics.
LastValueNaive
(name: str = 'LastValueNaive', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, **kwargs)¶ Bases:
autots.models.base.ModelObject
Naive forecasting predicting a dataframe of the last series value
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
-
fit
(df, future_regressor=None)¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Returns dict of new parameters for parameter tuning
-
get_params
()¶ Return dict of current parameters
-
predict
(forecast_length: int, future_regressor=None, just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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class
autots.models.basics.
Motif
(name: str = 'Motif', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, n_jobs: int = 1, window: int = 5, point_method: str = 'weighted_mean', distance_metric: str = 'minkowski', k: int = 10, max_windows: int = 5000, multivariate: bool = False, **kwargs)¶ Bases:
autots.models.base.ModelObject
Forecasts using a nearest neighbors type model adapted for probabilistic time series.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
-
fit
(df, future_regressor=None)¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Returns dict of new parameters for parameter tuning
-
get_params
()¶ Return dict of current parameters
-
predict
(forecast_length: int, future_regressor=None, just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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class
autots.models.basics.
MotifSimulation
(name: str = 'MotifSimulation', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, phrase_len: str = '5', comparison: str = 'magnitude_pct_change_sign', shared: bool = False, distance_metric: str = 'l2', max_motifs: float = 50, recency_weighting: float = 0.1, cutoff_threshold: float = 0.9, cutoff_minimum: int = 20, point_method: str = 'median', n_jobs: int = -1, verbose: int = 1, **kwargs)¶ Bases:
autots.models.base.ModelObject
More dark magic created by the evil mastermind of this project. Basically a highly-customized KNN
Warning: if you are forecasting many steps (large forecast_length), and interested in probabilistic upper/lower forecasts, then set recency_weighting <= 0, and have a larger cutoff_minimum
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
phrase_len (int) – length of motif vectors to compare as samples
comparison (str) – method to process data before comparison, ‘magnitude’ is original data
shared (bool) – whether to compare motifs across all series together, or separately
distance_metric (str) – passed through to sklearn pairwise_distances
max_motifs (float) – number of motifs to compare per series. If less 1, used as % of length training data
recency_weighting (float) – amount to the value of more recent data.
cutoff_threshold (float) – lowest value of distance metric to allow into forecast
cutoff_minimum (int) – minimum number of motif vectors to include in forecast.
point_method (str) – summarization method to choose forecast on, ‘sample’, ‘mean’, ‘sign_biased_mean’, ‘median’
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fit
(df, future_regressor=None)¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=None, just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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class
autots.models.basics.
NVAR
(name: str = 'NVAR', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, k: int = 2, ridge_param: float = 2.5e-06, warmup_pts: int = 1, seed_pts: int = 1, seed_weighted: str = None, batch_size: int = 5, batch_method: str = 'input_order', **kwargs)¶ Bases:
autots.models.base.ModelObject
Nonlinear Variable Autoregression or ‘Next-Generation Reservoir Computing’
based on https://github.com/quantinfo/ng-rc-paper-code/ Gauthier, D.J., Bollt, E., Griffith, A. et al. Next generation reservoir computing. Nat Commun 12, 5564 (2021). https://doi.org/10.1038/s41467-021-25801-2 with adjustments to make it probabilistic and to scale better
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
k (int) – the AR order (keep this small, larger is slow and usually pointless)
ridge_param (float) – standard lambda for ridge regression
warmup_pts (int) – in reality, passing 1 here (no warmup) is fine
batch_size (int) – nvar scales exponentially, to scale linearly, series are split into batches of size n
batch_method (str) – method for collecting series to make batches
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fit
(df, future_regressor=None)¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Returns dict of new parameters for parameter tuning
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=None, just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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class
autots.models.basics.
SeasonalNaive
(name: str = 'SeasonalNaive', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, lag_1: int = 7, lag_2: int = None, method: str = 'LastValue', **kwargs)¶ Bases:
autots.models.base.ModelObject
Naive forecasting predicting a dataframe with seasonal (lag) forecasts.
Concerto No. 2 in G minor, Op. 8, RV 315
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
method (str) – Either ‘LastValue’ (use last value of lag n) or ‘Mean’ (avg of all lag n)
lag_1 (int) – The lag of the seasonality, should int > 1.
lag_2 (int) – Optional second lag of seasonality which is averaged with first lag to produce forecast.
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fit
(df, future_regressor=None)¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=None, just_point_forecast: bool = False)¶ Generate forecast data immediately following dates of .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.basics.
ZeroesNaive
(name: str = 'ZeroesNaive', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, **kwargs)¶ Bases:
autots.models.base.ModelObject
Naive forecasting predicting a dataframe of zeroes (0’s)
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
-
fit
(df, future_regressor=None)¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Returns dict of new parameters for parameter tuning
-
get_params
()¶ Return dict of current parameters
-
predict
(forecast_length: int, future_regressor=None, just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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autots.models.basics.
looped_motif
(Xa, Xb, name, r_arr=None, window=10, distance_metric='minkowski', k=10, point_method='mean', prediction_interval=0.9)¶ inner function for Motif model.
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autots.models.basics.
predict_reservoir
(df, forecast_length, prediction_interval=None, warmup_pts=1, k=2, ridge_param=2.5e-06, seed_pts: int = 1, seed_weighted: str = None)¶ Nonlinear Variable Autoregression or ‘Next-Generation Reservoir Computing’
based on https://github.com/quantinfo/ng-rc-paper-code/ Gauthier, D.J., Bollt, E., Griffith, A. et al. Next generation reservoir computing. Nat Commun 12, 5564 (2021). https://doi.org/10.1038/s41467-021-25801-2 with adjustments to make it probabilistic
This is very slow and memory hungry when n series/dimensions gets big (ie > 50). Already effectively parallelized by linpack it seems. It’s very sensitive to error in most recent data point! The seed_pts and seed_weighted can help address that.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
k (int) – the AR order (keep this small, larger is slow and usually pointless)
ridge_param (float) – standard lambda for ridge regression
warmup_pts (int) – in reality, passing 1 here (no warmup) is fine
seed_pts (int) – number of back steps to use to simulate future if > 10, also increases search space of probabilistic forecast
seed_weighted (str) – how to summarize most recent points if seed_pts > 1
autots.models.dnn module¶
Neural Nets.
-
class
autots.models.dnn.
KerasRNN
(rnn_type: str = 'LSTM', kernel_initializer: str = 'lecun_uniform', hidden_layer_sizes: tuple = (32, 32, 32), optimizer: str = 'adam', loss: str = 'huber', epochs: int = 50, batch_size: int = 32, shape=1, verbose: int = 1, random_seed: int = 2020)¶ Bases:
object
Wrapper for Tensorflow Keras based RNN.
- Parameters
rnn_type (str) – Keras cell type ‘GRU’ or default ‘LSTM’
kernel_initializer (str) – passed to first keras LSTM or GRU layer
hidden_layer_sizes (tuple) – of len 1 or 3 passed to first keras LSTM or GRU layers
optimizer (str) – Passed to keras model.compile
loss (str) – Passed to keras model.compile
epochs (int) – Passed to keras model.fit
batch_size (int) – Passed to keras model.fit
verbose (int) – 0, 1 or 2. Passed to keras model.fit
random_seed (int) – passed to tf.random.set_seed()
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fit
(X, Y)¶ Train the model on dataframes of X and Y.
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predict
(X)¶ Predict on dataframe of X.
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class
autots.models.dnn.
Transformer
(head_size=256, num_heads=4, ff_dim=4, num_transformer_blocks=4, mlp_units=[128], mlp_dropout=0.4, dropout=0.25, optimizer: str = 'adam', loss: str = 'huber', epochs: int = 50, batch_size: int = 32, verbose: int = 1, random_seed: int = 2020)¶ Bases:
object
Wrapper for Tensorflow Keras based Transformer.
based on: https://keras.io/examples/timeseries/timeseries_transformer_classification/
- Parameters
optimizer (str) – Passed to keras model.compile
loss (str) – Passed to keras model.compile
epochs (int) – Passed to keras model.fit
batch_size (int) – Passed to keras model.fit
verbose (int) – 0, 1 or 2. Passed to keras model.fit
random_seed (int) – passed to tf.random.set_seed()
-
fit
(X, Y)¶ Train the model on dataframes of X and Y.
-
predict
(X)¶ Predict on dataframe of X.
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autots.models.dnn.
transformer_build_model
(input_shape, output_shape, head_size, num_heads, ff_dim, num_transformer_blocks, mlp_units, dropout=0, mlp_dropout=0)¶
-
autots.models.dnn.
transformer_encoder
(inputs, head_size, num_heads, ff_dim, dropout=0)¶
autots.models.ensemble module¶
Tools for generating and forecasting with ensembles of models.
-
autots.models.ensemble.
BestNEnsemble
(ensemble_params, forecasts_list, forecasts, lower_forecasts, upper_forecasts, forecasts_runtime, prediction_interval)¶ Generate mean forecast for ensemble of models.
-
autots.models.ensemble.
DistEnsemble
(ensemble_params, forecasts_list, forecasts, lower_forecasts, upper_forecasts, forecasts_runtime, prediction_interval)¶ Generate forecast for distance ensemble.
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autots.models.ensemble.
EnsembleForecast
(ensemble_str, ensemble_params, forecasts_list, forecasts, lower_forecasts, upper_forecasts, forecasts_runtime, prediction_interval, df_train=None, prematched_series: dict = None)¶ Return PredictionObject for given ensemble method.
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autots.models.ensemble.
EnsembleTemplateGenerator
(initial_results, forecast_length: int = 14, ensemble: str = 'simple')¶ Generate ensemble templates given a table of results.
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autots.models.ensemble.
HDistEnsemble
(ensemble_params, forecasts_list, forecasts, lower_forecasts, upper_forecasts, forecasts_runtime, prediction_interval)¶ Generate forecast for per_series per distance ensembling.
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autots.models.ensemble.
HorizontalEnsemble
(ensemble_params, forecasts_list, forecasts, lower_forecasts, upper_forecasts, forecasts_runtime, prediction_interval, df_train=None, prematched_series: dict = None)¶ Generate forecast for per_series ensembling.
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autots.models.ensemble.
HorizontalTemplateGenerator
(per_series, model_results, forecast_length: int = 14, ensemble: str = 'horizontal', subset_flag: bool = True, per_series2=None)¶ Generate horizontal ensemble templates given a table of results.
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autots.models.ensemble.
MosaicEnsemble
(ensemble_params, forecasts_list, forecasts, lower_forecasts, upper_forecasts, forecasts_runtime, prediction_interval, df_train=None, prematched_series: dict = None)¶ Generate forecast for mosaic ensembling.
- Parameters
prematched_series (dict) – from outer horizontal generalization, possibly different than params
-
autots.models.ensemble.
generalize_horizontal
(df_train, known_matches: dict, available_models: list, full_models: list = None)¶ generalize a horizontal model trained on a subset of all series
- Parameters
df_train (pd.DataFrame) – time series data
known_matches (dict) – series:model dictionary for some to all series
available_models (dict) – list of models actually available
full_models (dict) – models that are available for every single series
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autots.models.ensemble.
generate_mosaic_template
(initial_results, full_mae_ids, num_validations, col_names, full_mae_errors, **kwargs)¶ Generate an ensemble template from results.
-
autots.models.ensemble.
horizontal_classifier
(df_train, known: dict, method: str = 'whatever')¶ CLassify unknown series with the appropriate model for horizontal ensembling.
- Parameters
df_train (pandas.DataFrame) – historical data about the series. Columns = series_ids.
known (dict) – dict of series_id: classifier outcome including some but not all series in df_train.
- Returns
dict.
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autots.models.ensemble.
mosaic_classifier
(df_train, known)¶ CLassify unknown series with the appropriate model for mosaic ensembles.
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autots.models.ensemble.
mosaic_or_horizontal
(all_series: dict)¶ Take a mosaic or horizontal model and return series or models.
- Parameters
all_series (dict) – dict of series: model (or list of models)
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autots.models.ensemble.
mosaic_to_horizontal
(ModelParameters, forecast_period: int = 0)¶ Take a mosaic template and pull a single forecast step as a horizontal model.
- Parameters
ModelParameters (dict) – the json.loads() of the ModelParameters of a mosaic ensemble template
forecast_period (int) – when to choose the model, starting with 0 where 0 would be the first forecast datestamp, 1 would be the second, and so on must be less than forecast_length that the model was trained on.
- Returs:
ModelParameters (dict)
-
autots.models.ensemble.
parse_horizontal
(all_series: dict, model_id: str = None, series_id: str = None)¶ Take a mosaic or horizontal model and return series or models.
- Parameters
all_series (dict) – dict of series: model (or list of models)
model_id (str) – name of model to find series for
series_id (str) – name of series to find models for
- Returns
list
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autots.models.ensemble.
summarize_series
(df)¶ Summarize time series data. For now just df.describe().
autots.models.gluonts module¶
GluonTS
Excellent models, released by Amazon, scale well. Except it is really the only thing I use that runs mxnet, and it takes a while to train these guys…
-
class
autots.models.gluonts.
GluonTS
(name: str = 'GluonTS', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, gluon_model: str = 'DeepAR', epochs: int = 20, learning_rate: float = 0.001, context_length=10, forecast_length: int = 14, **kwargs)¶ Bases:
autots.models.base.ModelObject
GluonTS based on mxnet.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – Not yet implemented
gluon_model (str) – Model Structure to Use - [‘DeepAR’, ‘NPTS’, ‘DeepState’, ‘WaveNet’,’DeepFactor’, ‘Transformer’,’SFF’, ‘MQCNN’, ‘DeepVAR’, ‘GPVAR’, ‘NBEATS’]
epochs (int) – Number of neural network training epochs. Higher generally results in better, then over fit.
learning_rate (float) – Neural net training parameter
context_length (str) – int window, ‘2ForecastLength’, or ‘nForecastLength’
forecast_length (int) – Length to forecast. Unlike in other methods, this must be provided before fitting model
-
fit
(df, future_regressor=None)¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
autots.models.greykite module¶
Greykite.
-
class
autots.models.greykite.
Greykite
(name: str = 'Greykite', frequency: str = 'infer', prediction_interval: float = 0.9, holiday: bool = False, growth: str = None, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, n_jobs: int = None)¶ Bases:
autots.models.base.ModelObject
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
holiday (bool) – If true, include holidays
regression_type (str) – type of regression (None, ‘User’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
autots.models.greykite.
seek_the_oracle
(df_index, series, col, forecast_length, freq, prediction_interval=0.9, model_template='silverkite', growth=None, holiday=True, holiday_country='UnitedStates', regressors=None, verbose=0, inner_n_jobs=1, **kwargs)¶ Internal. For loop or parallel version of Greykite.
autots.models.model_list module¶
Lists of models grouped by aspects.
-
autots.models.model_list.
auto_model_list
(n_jobs, n_series, frequency)¶
autots.models.prophet module¶
Facebook’s Prophet
Since Prophet install can be finicky on Windows, it will be an optional dependency.
-
class
autots.models.prophet.
FBProphet
(name: str = 'FBProphet', frequency: str = 'infer', prediction_interval: float = 0.9, holiday: bool = False, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, n_jobs: int = None)¶ Bases:
autots.models.base.ModelObject
Facebook’s Prophet
‘thou shall count to 3, no more, no less, 3 shall be the number thou shall count, and the number of the counting shall be 3. 4 thou shall not count, neither count thou 2, excepting that thou then preceed to 3.’ -Python
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
holiday (bool) – If true, include holidays
regression_type (str) – type of regression (None, ‘User’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
autots.models.prophet.
seek_the_oracle
(current_series, args, series, forecast_length, future_regressor)¶ Prophet for for loop or parallel.
autots.models.sklearn module¶
Sklearn dependent models
Decision Tree, Elastic Net, Random Forest, MLPRegressor, KNN, Adaboost
-
class
autots.models.sklearn.
ComponentAnalysis
(name: str = 'ComponentAnalysis', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, n_components: int = 10, forecast_length: int = 14, model: str = 'GLS', model_parameters: dict = {}, decomposition: str = 'PCA', n_jobs: int = -1)¶ Bases:
autots.models.base.ModelObject
Forecasting on principle components.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
model (str) – An AutoTS model str
model_parameters (dict) – parameters to pass to AutoTS model
n_components (int) – int or ‘NthN’ number of components to use
decomposition (str) – decomposition method to use from scikit-learn
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generate forecast data immediately following dates of .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.sklearn.
DatepartRegression
(name: str = 'DatepartRegression', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, forecast_length: int = 1, n_jobs: int = None, regression_model: dict = {'model': 'DecisionTree', 'model_params': {'max_depth': 5, 'min_samples_split': 2}}, datepart_method: str = 'expanded', regression_type: str = None, **kwargs)¶ Bases:
autots.models.base.ModelObject
Regression not on series but datetime
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generate forecast data immediately following dates of index supplied to .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
future_regressor (pandas.DataFrame or Series) – Datetime Indexed
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.sklearn.
MultivariateRegression
(name: str = 'MultivariateRegression', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', verbose: int = 0, random_seed: int = 2020, forecast_length: int = 7, regression_model: dict = {'model': 'RandomForest', 'model_params': {}}, holiday: bool = False, mean_rolling_periods: int = 30, macd_periods: int = None, std_rolling_periods: int = 7, max_rolling_periods: int = 7, min_rolling_periods: int = 7, ewm_var_alpha: float = None, quantile90_rolling_periods: int = None, quantile10_rolling_periods: int = None, ewm_alpha: float = 0.5, additional_lag_periods: int = 7, abs_energy: bool = False, rolling_autocorr_periods: int = None, datepart_method: str = None, polynomial_degree: int = None, window: int = None, n_jobs: int = -1, **kwargs)¶ Bases:
autots.models.base.ModelObject
Regression-framed approach to forecasting using sklearn. A multiariate version of rolling regression: ie each series is agged independently but modeled together
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
holiday (bool) – If true, include holiday flags
regression_type (str) – type of regression (None, ‘User’)
-
fit
(df, future_regressor=None)¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
future_regressor (pandas.DataFrame or Series) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int = None, just_point_forecast: bool = False, future_regressor=None)¶ Generate forecast data immediately following dates of index supplied to .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead ignored here for this model, must be set in __init__ before .fit()
future_regressor (pd.DataFrame) – additional regressor
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.sklearn.
RollingRegression
(name: str = 'RollingRegression', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', verbose: int = 0, random_seed: int = 2020, regression_model: dict = {'model': 'ExtraTrees', 'model_params': {}}, holiday: bool = False, mean_rolling_periods: int = 30, macd_periods: int = None, std_rolling_periods: int = 7, max_rolling_periods: int = 7, min_rolling_periods: int = 7, ewm_var_alpha: int = None, quantile90_rolling_periods: int = None, quantile10_rolling_periods: int = None, ewm_alpha: float = 0.5, additional_lag_periods: int = 7, abs_energy: bool = False, rolling_autocorr_periods: int = None, add_date_part: str = None, polynomial_degree: int = None, x_transform: str = None, window: int = None, n_jobs: int = -1, **kwargs)¶ Bases:
autots.models.base.ModelObject
General regression-framed approach to forecasting using sklearn.
Who are you who are so wise in the ways of science? I am Arthur, King of the Britons. -Python
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
holiday (bool) – If true, include holiday flags
regression_type (str) – type of regression (None, ‘User’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
future_regressor (pandas.DataFrame or Series) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generate forecast data immediately following dates of index supplied to .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.sklearn.
UnivariateRegression
(name: str = 'UnivariateRegression', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', verbose: int = 0, random_seed: int = 2020, forecast_length: int = 7, regression_model: dict = {'model': 'ExtraTrees', 'model_params': {}}, holiday: bool = False, mean_rolling_periods: int = 30, macd_periods: int = None, std_rolling_periods: int = 7, max_rolling_periods: int = 7, min_rolling_periods: int = 7, ewm_var_alpha: float = None, ewm_alpha: float = 0.5, additional_lag_periods: int = 7, abs_energy: bool = False, rolling_autocorr_periods: int = None, add_date_part: str = None, polynomial_degree: int = None, x_transform: str = None, window: int = None, n_jobs: int = -1, **kwargs)¶ Bases:
autots.models.base.ModelObject
Regression-framed approach to forecasting using sklearn. A univariate version of rolling regression: ie each series is modeled independently
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
holiday (bool) – If true, include holiday flags
regression_type (str) – type of regression (None, ‘User’)
-
fit
(df, future_regressor=None)¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
future_regressor (pandas.DataFrame or Series) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int = None, just_point_forecast: bool = False, future_regressor=None)¶ Generate forecast data immediately following dates of index supplied to .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead ignored here for this model, must be set in __init__ before .fit()
future_regressor (pd.DataFrame) – additional regressor
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.sklearn.
WindowRegression
(name: str = 'WindowRegression', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2022, verbose: int = 0, window_size: int = 10, regression_model: dict = {'model': 'RandomForest', 'model_params': {}}, input_dim: str = 'univariate', output_dim: str = 'forecast_length', normalize_window: bool = False, shuffle: bool = False, forecast_length: int = 1, max_windows: int = 5000, regression_type: str = None, n_jobs: int = -1, **kwargs)¶ Bases:
autots.models.base.ModelObject
Regression use the last n values as the basis of training data.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (#) – str = None,
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generate forecast data immediately following dates of .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
autots.models.sklearn.
generate_regressor_params
(model_dict=None)¶
-
autots.models.sklearn.
last_window
(df, window_size: int = 10, input_dim: str = 'univariate', normalize_window: bool = False)¶
-
autots.models.sklearn.
retrieve_regressor
(regression_model: dict = {'model': 'Adaboost', 'model_params': {'base_estimator': 'DecisionTree', 'learning_rate': 1.0, 'loss': 'linear', 'n_estimators': 50}}, verbose: int = 0, verbose_bool: bool = False, random_seed: int = 2020, n_jobs: int = 1, multioutput: bool = True)¶ Convert a model param dict to model object for regression frameworks.
-
autots.models.sklearn.
rolling_x_regressor
(df, mean_rolling_periods: int = 30, macd_periods: int = None, std_rolling_periods: int = 7, max_rolling_periods: int = None, min_rolling_periods: int = None, quantile90_rolling_periods: int = None, quantile10_rolling_periods: int = None, ewm_alpha: float = 0.5, ewm_var_alpha: float = None, additional_lag_periods: int = 7, abs_energy: bool = False, rolling_autocorr_periods: int = None, add_date_part: str = None, holiday: bool = False, holiday_country: str = 'US', polynomial_degree: int = None, window: int = None)¶ Generate more features from initial time series.
macd_periods ignored if mean_rolling is None.
Returns a dataframe of statistical features. Will need to be shifted by 1 or more to match Y for forecast.
-
autots.models.sklearn.
rolling_x_regressor_regressor
(df, mean_rolling_periods: int = 30, macd_periods: int = None, std_rolling_periods: int = 7, max_rolling_periods: int = None, min_rolling_periods: int = None, quantile90_rolling_periods: int = None, quantile10_rolling_periods: int = None, ewm_alpha: float = 0.5, ewm_var_alpha: float = None, additional_lag_periods: int = 7, abs_energy: bool = False, rolling_autocorr_periods: int = None, add_date_part: str = None, holiday: bool = False, holiday_country: str = 'US', polynomial_degree: int = None, window: int = None, future_regressor=None)¶ Adds in the future_regressor.
-
autots.models.sklearn.
window_maker
(df, window_size: int = 10, input_dim: str = 'univariate', normalize_window: bool = False, shuffle: bool = False, output_dim: str = 'forecast_length', forecast_length: int = 1, max_windows: int = 5000, regression_type: str = None, future_regressor=None, random_seed: int = 1234)¶ Convert a dataset into slices with history and y forecast.
- Parameters
df (pd.DataFrame) – wide format df with sorted index
window_size (int) – length of history to use for X window
input_dim (str) – univariate or multivariate. If multivariate, all series in single X row
shuffle (bool) – (deprecated)
output_dim (str) – ‘forecast_length’ or ‘1step’ where 1 step is basically forecast_length=1
forecast_length (int) – number of periods ahead that will be forecast
max_windows (int) – a cap on total number of windows to generate. If exceeded, random of this int are selected.
regression_type (str) – None or “user” if to try to concat regressor to windows
future_regressor (pd.DataFrame) – values of regressor if used
random_seed (int) – a consistent random
- Returns
X, Y
autots.models.statsmodels module¶
Statsmodels based forecasting models.
-
class
autots.models.statsmodels.
ARIMA
(name: str = 'ARIMA', frequency: str = 'infer', prediction_interval: float = 0.9, p: int = 0, d: int = 1, q: int = 0, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, n_jobs: int = None, **kwargs)¶ Bases:
autots.models.base.ModelObject
ARIMA from Statsmodels.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
p (int) – is the number of autoregressive steps,
d (int) – is the number of differences needed for stationarity
q (int) – is the number of lagged forecast errors in the prediction.
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
n_jobs (int) – passed to joblib for multiprocessing. Set to none for context manager.
-
fit
(df, future_regressor=None)¶ Train algorithm given data supplied .
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
large p,d,q can be very slow (a p of 30 can take hours)
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=None, just_point_forecast=False)¶ Generate forecast data immediately following dates of index supplied to .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
DynamicFactor
(name: str = 'DynamicFactor', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, k_factors: int = 1, factor_order: int = 0, **kwargs)¶ Bases:
autots.models.base.ModelObject
DynamicFactor from Statsmodels
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
- Either a PredictionObject of forecasts and metadata, or
if just_point_forecast == True, a dataframe of point forecasts
maModel = DynamicFactor(df_train, freq = ‘MS’, k_factors = 2, factor_order=2).fit() maPred = maModel.predict()
-
class
autots.models.statsmodels.
ETS
(name: str = 'ETS', frequency: str = 'infer', prediction_interval: float = 0.9, damped_trend: bool = False, trend: str = None, seasonal: str = None, seasonal_periods: int = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, n_jobs: int = None, **kwargs)¶ Bases:
autots.models.base.ModelObject
Exponential Smoothing from Statsmodels
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
damped_trend (bool) – passed through to statsmodel ETS (formerly just ‘damped’)
trend (str) – passed through to statsmodel ETS
seasonal (bool) – passed through to statsmodel ETS
seasonal_periods (int) – passed through to statsmodel ETS
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
GLM
(name: str = 'GLM', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, regression_type: str = None, family='Gaussian', constant: bool = False, verbose: int = 1, n_jobs: int = None, **kwargs)¶ Bases:
autots.models.base.ModelObject
Simple linear regression from statsmodels
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
GLS
(name: str = 'GLS', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, **kwargs)¶ Bases:
autots.models.base.ModelObject
Simple linear regression from statsmodels
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Returns dict of new parameters for parameter tuning
-
get_params
()¶ Return dict of current parameters
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
UnobservedComponents
(name: str = 'UnobservedComponents', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, n_jobs: int = 1, level: bool = False, trend: bool = False, cycle: bool = False, damped_cycle: bool = False, irregular: bool = False, stochastic_cycle: bool = False, stochastic_trend: bool = False, stochastic_level: bool = False, **kwargs)¶ Bases:
autots.models.base.ModelObject
UnobservedComponents from Statsmodels.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generate forecast data immediately following dates of index supplied to .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
VAR
(name: str = 'VAR', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, maxlags: int = 15, ic: str = 'fpe', **kwargs)¶ Bases:
autots.models.base.ModelObject
VAR from Statsmodels.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
VARMAX
(name: str = 'VARMAX', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, order: tuple = (1, 0), trend: str = 'c', **kwargs)¶ Bases:
autots.models.base.ModelObject
VARMAX from Statsmodels
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generate forecast data immediately following dates of index supplied to .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
VECM
(name: str = 'VECM', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, deterministic: str = 'nc', k_ar_diff: int = 1, **kwargs)¶ Bases:
autots.models.base.ModelObject
VECM from Statsmodels
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
-
fit
(df, future_regressor=None)¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=None, just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
autots.models.statsmodels.
arima_seek_the_oracle
(current_series, args, series)¶
-
autots.models.statsmodels.
glm_forecast_by_column
(current_series, X, Xf, args, col)¶ Run one series of GLM and return prediction.
autots.models.tfp module¶
-
class
autots.models.tfp.
TFPRegression
(name: str = 'TFPRegression', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 1, kernel_initializer: str = 'lecun_uniform', optimizer: str = 'adam', loss: str = 'negloglike', epochs: int = 50, batch_size: int = 32, dist: str = 'normal', regression_type: str = None)¶ Bases:
autots.models.base.ModelObject
Tensorflow Probability regression.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.tfp.
TFPRegressor
(kernel_initializer: str = 'lecun_uniform', optimizer: str = 'adam', loss: str = 'negloglike', epochs: int = 50, batch_size: int = 32, dist: str = 'normal', verbose: int = 1, random_seed: int = 2020)¶ Bases:
object
Wrapper for Tensorflow Keras based RNN.
- Parameters
rnn_type (str) – Keras cell type ‘GRU’ or default ‘LSTM’
kernel_initializer (str) – passed to first keras LSTM or GRU layer
hidden_layer_sizes (tuple) – of len 1 or 3 passed to first keras LSTM or GRU layers
optimizer (str) – Passed to keras model.compile
loss (str) – Passed to keras model.compile
epochs (int) – Passed to keras model.fit
batch_size (int) – Passed to keras model.fit
verbose (int) – 0, 1 or 2. Passed to keras model.fit
random_seed (int) – passed to tf.random.set_seed()
-
fit
(X, Y)¶ Train the model on dataframes of X and Y.
-
predict
(X, conf_int: float = None)¶ Predict on dataframe of X.
-
class
autots.models.tfp.
TensorflowSTS
(name: str = 'TensorflowSTS', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, trend: str = 'local', seasonal_periods: int = None, ar_order: int = None, fit_method: str = 'hmc', num_steps: int = 200)¶ Bases:
autots.models.base.ModelObject
STS from TensorflowProbability.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
Module contents¶
Model Models