autots package¶
Subpackages¶
- autots.datasets package
- autots.evaluator package
- autots.models package
- Submodules
- autots.models.base module
- autots.models.basics module
- autots.models.dnn module
- autots.models.ensemble module
- autots.models.gluonts module
- autots.models.greykite module
- autots.models.model_list module
- autots.models.prophet module
- autots.models.sklearn module
- autots.models.statsmodels module
- autots.models.tfp module
- Module contents
- autots.templates package
- autots.tools package
- Submodules
- autots.tools.cpu_count module
- autots.tools.hierarchial module
- autots.tools.holiday module
- autots.tools.impute module
- autots.tools.probabilistic module
- autots.tools.profile module
- autots.tools.regressor module
- autots.tools.seasonal module
- autots.tools.shaping module
- autots.tools.transform module
- Module contents
Module contents¶
Automated Time Series Model Selection for Python
https://github.com/winedarksea/AutoTS
-
autots.
load_daily
(long: bool = True)¶ 2020 Covid, Air Pollution, and Economic Data.
Sources: Covid Tracking Project, EPA, and FRED
- Parameters
long (bool) – if True, return data in long format. Otherwise return wide
-
autots.
load_monthly
(long: bool = True)¶ Federal Reserve of St. Louis monthly economic indicators.
-
autots.
load_yearly
(long: bool = True)¶ Federal Reserve of St. Louis annual economic indicators.
-
autots.
load_hourly
(long: bool = True)¶ Traffic data from the MN DOT via the UCI data repository.
-
autots.
load_weekly
(long: bool = True)¶ Weekly petroleum industry data from the EIA.
-
autots.
load_weekdays
(long: bool = False, categorical: bool = True, periods: int = 180)¶ Test edge cases by creating a Series with values as day of week.
- Parameters
long (bool) – if True, return a df with columns “value” and “datetime” if False, return a Series with dt index
categorical (bool) – if True, return str/object, else return int
periods (int) – number of periods, ie length of data to generate
-
autots.
load_live_daily
(long: bool = False, fred_key: str = None, fred_series: list = ['DGS10', 'T5YIE', 'SP500', 'DCOILWTICO', 'DEXUSEU'], tickers: list = ['MSFT'], trends_list: list = ['forecasting', 'cycling', 'cpu', 'microsoft'], weather_data_types: list = ['AWND', 'WSF2', 'TAVG'], weather_stations: list = ['USW00094846', 'USW00014925'], weather_years: int = 10, london_air_stations: list = ['CT3', 'SK8'], london_air_species: str = 'PM25', london_air_days: int = 180, earthquake_days: int = 180, earthquake_min_magnitude: int = 5)¶ Generates a dataframe of data up to the present day.
- Parameters
long (bool) – whether to return in long format or wide
fred_key (str) – https://fred.stlouisfed.org/docs/api/api_key.html
fred_series (list) – list of FRED series IDs. This requires fredapi package
tickers (list) – list of stock tickers, requires yfinance
trends (list) – list of search keywords, requires pytrends.
weather_data_types (list) – from NCEI NOAA api data types, GHCN Daily Weather Elements PRCP, SNOW, TMAX, TMIN, TAVG, AWND, WSF1, WSF2, WSF5, WSFG
weather_stations (list) – from NCEI NOAA api station ids
london_air_stations (list) – londonair.org.uk source station IDs
london_species (str) – what measurement to pull from London Air. Not all stations have all metrics. earthquake_min_magnitude (int): smallest earthquake magnitude to pull from earthquake.usgs.gov
-
autots.
load_linear
(long=False, shape=None, start_date: str = '2021-01-01', introduce_nan: float = None)¶ Create a dataset of just zeroes for testing edge case.
-
class
autots.
AutoTS
(forecast_length: int = 14, frequency: str = 'infer', prediction_interval: float = 0.9, max_generations: int = 10, no_negatives: bool = False, constraint: float = None, ensemble: str = 'auto', initial_template: str = 'General+Random', random_seed: int = 2020, holiday_country: str = 'US', subset: int = None, aggfunc: str = 'first', na_tolerance: float = 1, metric_weighting: dict = {'containment_weighting': 0, 'contour_weighting': 1, 'mae_weighting': 2, 'rmse_weighting': 2, 'runtime_weighting': 0.05, 'smape_weighting': 10, 'spl_weighting': 2}, drop_most_recent: int = 0, drop_data_older_than_periods: int = 100000, model_list: str = 'default', transformer_list: dict = 'fast', transformer_max_depth: int = 6, num_validations: int = 2, models_to_validate: float = 0.15, max_per_model_class: int = None, validation_method: str = 'backwards', min_allowed_train_percent: float = 0.5, remove_leading_zeroes: bool = False, prefill_na: str = None, introduce_na: bool = None, model_interrupt: bool = False, verbose: int = 1, n_jobs: int = None)¶ Bases:
object
Automate time series modeling using a genetic algorithm.
- Parameters
forecast_length (int) – number of periods over which to evaluate forecast. Can be overriden later in .predict().
frequency (str) – ‘infer’ or a specific pandas datetime offset. Can be used to force rollup of data (ie daily input, but frequency ‘M’ will rollup to monthly).
prediction_interval (float) – 0-1, uncertainty range for upper and lower forecasts. Adjust range, but rarely matches actual containment.
max_generations (int) – number of genetic algorithms generations to run. More runs = longer runtime, generally better accuracy. It’s called max because someday there will be an auto early stopping option, but for now this is just the exact number of generations to run.
no_negatives (bool) – if True, all negative predictions are rounded up to 0.
constraint (float) – when not None, use this value * data st dev above max or below min for constraining forecast values. Applied to point forecast only, not upper/lower forecasts.
ensemble (str) – None or list or comma-separated string containing: ‘auto’, ‘simple’, ‘distance’, ‘horizontal’, ‘horizontal-min’, ‘horizontal-max’, “mosaic”
initial_template (str) – ‘Random’ - randomly generates starting template, ‘General’ uses template included in package, ‘General+Random’ - both of previous. Also can be overriden with self.import_template()
random_seed (int) – random seed allows (slightly) more consistent results.
holiday_country (str) – passed through to Holidays package for some models.
subset (int) – maximum number of series to evaluate at once. Useful to speed evaluation when many series are input. takes a new subset of columns on each validation, unless mosaic ensembling, in which case columns are the same in each validation
aggfunc (str) – if data is to be rolled up to a higher frequency (daily -> monthly) or duplicate timestamps are included. Default ‘first’ removes duplicates, for rollup try ‘mean’ or np.sum. Beware numeric aggregations like ‘mean’ will not work with non-numeric inputs.
na_tolerance (float) – 0 to 1. Series are dropped if they have more than this percent NaN. 0.95 here would allow series containing up to 95% NaN values.
metric_weighting (dict) – weights to assign to metrics, effecting how the ranking score is generated.
drop_most_recent (int) – option to drop n most recent data points. Useful, say, for monthly sales data where the current (unfinished) month is included. occurs after any aggregration is applied, so will be whatever is specified by frequency, will drop n frequencies
drop_data_older_than_periods (int) – take only the n most recent timestamps
model_list (list) – str alias or list of names of model objects to use
transformer_list (list) – list of transformers to use, or dict of transformer:probability. Note this does not apply to initial templates. can accept string aliases: “all”, “fast”, “superfast”
transformer_max_depth (int) – maximum number of sequential transformers to generate for new Random Transformers. Fewer will be faster.
num_validations (int) – number of cross validations to perform. 0 for just train/test on final split.
models_to_validate (int) – top n models to pass through to cross validation. Or float in 0 to 1 as % of tried. 0.99 is forced to 100% validation. 1 evaluates just 1 model. If horizontal or probabilistic ensemble, then additional min per_series models above the number here may be added to validation.
max_per_model_class (int) – of the models_to_validate what is the maximum to pass from any one model class/family.
validation_method (str) – ‘even’, ‘backwards’, or ‘seasonal n’ where n is an integer of seasonal ‘backwards’ is better for recency and for shorter training sets ‘even’ splits the data into equally-sized slices best for more consistent data ‘seasonal n’ for example ‘seasonal 364’ would test all data on each previous year of the forecast_length that would immediately follow the training data.
min_allowed_train_percent (float) – percent of forecast length to allow as min training, else raises error. 0.5 with a forecast length of 10 would mean 5 training points are mandated, for a total of 15 points. Useful in (unrecommended) cases where forecast_length > training length.
remove_leading_zeroes (bool) – replace leading zeroes with NaN. Useful in data where initial zeroes mean data collection hasn’t started yet.
prefill_na (str) – value to input to fill all NaNs with. Leaving as None and allowing model interpolation is recommended. None, 0, ‘mean’, or ‘median’. 0 may be useful in for examples sales cases where all NaN can be assumed equal to zero.
introduce_na (bool) – whether to force last values in one training validation to be NaN. Helps make more robust models. defaults to None, which introduces NaN in last rows of validations if any NaN in tail of training data. Will not introduce NaN to all series if subset is used. if True, will also randomly change 20% of all rows to NaN in the validations
model_interrupt (bool) – if False, KeyboardInterrupts quit entire program. if True, KeyboardInterrupts attempt to only quit current model. if True, recommend use in conjunction with verbose > 0 and result_file in the event of accidental complete termination.
verbose (int) – setting to 0 or lower should reduce most output. Higher numbers give more output.
n_jobs (int) – Number of cores available to pass to parallel processing. A joblib context manager can be used instead (pass None in this case). Also ‘auto’.
-
best_model
¶ DataFrame containing template for the best ranked model
- Type
pd.DataFrame
-
best_model_name
¶ model name
- Type
str
-
best_model_params
¶ model params
- Type
dict
-
best_model_transformation_params
¶ transformation parameters
- Type
dict
-
best_model_ensemble
¶ Ensemble type int id
- Type
int
-
regression_check
¶ If True, the best_model uses an input ‘User’ future_regressor
- Type
bool
-
df_wide_numeric
¶ dataframe containing shaped final data
- Type
pd.DataFrame
-
model_results
¶ contains a collection of result metrics
- Type
object
-
fit, predict
-
export_template, import_template, import_results
-
results, failure_rate
-
horizontal_to_df, mosaic_to_df
-
plot_horizontal, plot_horizontal_transformers, plot_generation_loss, plot_backforecast
-
back_forecast
(column=None, n_splits: int = 3, tail: int = None, verbose: int = 0)¶ Create forecasts for the historical training data, ie. backcast or back forecast.
This actually forecasts on historical data, these are not fit model values as are often returned by other packages. As such, this will be slower, but more representative of real world model performance. There may be jumps in data between chunks.
Args are same as for model_forecast except… n_splits(int): how many pieces to split data into. Pass 2 for fastest, or “auto” for best accuracy column (str): if to run on only one column, pass column name. Faster than full. tail (int): df.tail() of the dataset, back_forecast is only run on n most recent observations.
Returns a standard prediction object (access .forecast, .lower_forecast, .upper_forecast)
-
export_template
(filename=None, models: str = 'best', n: int = 5, max_per_model_class: int = None, include_results: bool = False)¶ Export top results as a reusable template.
- Parameters
filename (str) – ‘csv’ or ‘json’ (in filename). None to return a dataframe and not write a file.
models (str) – ‘best’ or ‘all’
n (int) – if models = ‘best’, how many n-best to export
max_per_model_class (int) – if models = ‘best’, the max number of each model class to include in template
include_results (bool) – whether to include performance metrics
-
failure_rate
(result_set: str = 'initial')¶ Return fraction of models passing with exceptions.
- Parameters
result_set (str, optional) – ‘validation’ or ‘initial’. Defaults to ‘initial’.
- Returns
float.
-
fit
(df, date_col: str = None, value_col: str = None, id_col: str = None, future_regressor=[], weights: dict = {}, result_file: str = None, grouping_ids=None)¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed dataframe of series, or dataframe of three columns as below.
date_col (str) – name of datetime column
value_col (str) – name of column containing the data of series.
id_col (str) – name of column identifying different series.
future_regressor (numpy.Array) – single external regressor matching train.index
weights (dict) – {‘colname1’: 2, ‘colname2’: 5} - increase importance of a series in metric evaluation. Any left blank assumed to have weight of 1. pass the alias ‘mean’ as a str ie weights=’mean’ to automatically use the mean value of a series as its weight available aliases: mean, median, min, max
result_file (str) – results saved on each new generation. Does not include validation rounds. “.csv” save model results table. “.pickle” saves full object, including ensemble information.
grouping_ids (dict) – currently a one-level dict containing series_id:group_id mapping. used in 0.2.x but not 0.3.x+ versions. retained for potential future use
-
horizontal_to_df
()¶ helper function for plotting.
-
import_results
(filename)¶ Add results from another run on the same data.
Input can be filename with .csv or .pickle. or can be a DataFrame of model results or a full TemplateEvalObject
-
import_template
(filename: str, method: str = 'add_on', enforce_model_list: bool = True)¶ Import a previously exported template of model parameters. Must be done before the AutoTS object is .fit().
- Parameters
filename (str) – file location (or a pd.DataFrame already loaded)
method (str) – ‘add_on’ or ‘only’ - “add_on” keeps initial_template generated in init. “only” uses only this template.
enforce_model_list (bool) – if True, remove model types not in model_list
-
mosaic_to_df
()¶ Helper function to create a readable df of models in mosaic.
-
plot_backforecast
(series=None, n_splits: int = 3, start_date=None, **kwargs)¶ Plot the historical data and fit forecast on historic.
- Parameters
series (str or list) – column names of time series
n_splits (int or str) – “auto”, number > 2, higher more accurate but slower
passed to pd.DataFrame.plot() (**kwargs) –
-
plot_generation_loss
(**kwargs)¶ Plot improvement in accuracy over generations. Note: this is only “one size fits all” accuracy and doesn’t account for the benefits seen for ensembling.
- Parameters
passed to pd.DataFrame.plot() (**kwargs) –
-
plot_horizontal
(max_series: int = 20, **kwargs)¶ Simple plot to visualize assigned series: models.
Note that for ‘mosiac’ ensembles, it only plots the type of the most common model_id for that series, or the first if all are mode.
- Parameters
max_series (int) – max number of points to plot
passed to pandas.plot() (**kwargs) –
-
plot_horizontal_transformers
(method='transformers', color_list=None, **kwargs)¶ Simple plot to visualize transformers used. Note this doesn’t capture transformers nested in simple ensembles.
- Parameters
method (str) – ‘fillna’ or ‘transformers’ - which to plot
= list of colors to sample for bar colors. Can be names or hex. (color_list) –
passed to pandas.plot() (**kwargs) –
-
predict
(forecast_length: int = 'self', prediction_interval: float = 'self', future_regressor=[], hierarchy=None, just_point_forecast: bool = False, verbose: int = 'self')¶ Generate forecast data immediately following dates of index supplied to .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
prediction_interval (float) – interval of upper/lower forecasts. defaults to ‘self’ ie the interval specified in __init__() if prediction_interval is a list, then returns a dict of forecast objects.
future_regressor (numpy.Array) – additional regressor
hierarchy – Not yet implemented
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
-
results
(result_set: str = 'initial')¶ Convenience function to return tested models table.
- Parameters
result_set (str) – ‘validation’ or ‘initial’
-
autots.
TransformTS
¶
-
class
autots.
GeneralTransformer
(fillna: str = 'ffill', transformations: dict = {}, transformation_params: dict = {}, grouping: str = None, reconciliation: str = None, grouping_ids=None, random_seed: int = 2020)¶ Bases:
object
Remove fillNA and then mathematical transformations.
Expects a chronologically sorted pandas.DataFrame with a DatetimeIndex, only numeric data, and a ‘wide’ (one column per series) shape.
Warning
- inverse_transform will not fully return the original data under many conditions
the primary intention of inverse_transform is to inverse for forecast (immediately following the historical time period) data from models, not to return original data
NAs filled will be returned with the filled value
Discretization, statsmodels filters, Round, Slice, ClipOutliers cannot be inversed
- RollingMean, PctChange, CumSum, Seasonal Difference, and DifferencedTransformer will only return original or an immediately following forecast
by default ‘forecast’ is expected, ‘original’ can be set in trans_method
- Parameters
fillNA (str) –
method to fill NA, passed through to FillNA()
’ffill’ - fill most recent non-na value forward until another non-na value is reached ‘zero’ - fill with zero. Useful for sales and other data where NA does usually mean $0. ‘mean’ - fill all missing values with the series’ overall average value ‘median’ - fill all missing values with the series’ overall median value ‘rolling_mean’ - fill with last n (window = 10) values ‘rolling_mean_24’ - fill with avg of last 24 ‘ffill_mean_biased’ - simple avg of ffill and mean ‘fake_date’ - shifts forward data over nan, thus values will have incorrect timestamps ‘IterativeImputer’ - sklearn iterative imputer most of the interpolate methods from pandas.interpolate
transformations (dict) –
transformations to apply {0: “MinMaxScaler”, 1: “Detrend”, …}
’None’ ‘MinMaxScaler’ - Sklearn MinMaxScaler ‘PowerTransformer’ - Sklearn PowerTransformer ‘QuantileTransformer’ - Sklearn ‘MaxAbsScaler’ - Sklearn ‘StandardScaler’ - Sklearn ‘RobustScaler’ - Sklearn ‘PCA, ‘FastICA’ - performs sklearn decomposition and returns n-cols worth of n_components ‘Detrend’ - fit then remove a linear regression from the data ‘RollingMeanTransformer’ - 10 period rolling average, can receive a custom window by transformation_param if used as second_transformation ‘FixedRollingMean’ - same as RollingMean, but with inverse_transform disabled, so smoothed forecasts are maintained. ‘RollingMean10’ - 10 period rolling average (smoothing) ‘RollingMean100thN’ - Rolling mean of periods of len(train)/100 (minimum 2) ‘DifferencedTransformer’ - makes each value the difference of that value and the previous value ‘PctChangeTransformer’ - converts to pct_change, not recommended if lots of zeroes in data ‘SinTrend’ - removes a sin trend (fitted to each column) from the data ‘CumSumTransformer’ - makes value sum of all previous ‘PositiveShift’ - makes all values >= 1 ‘Log’ - log transform (uses PositiveShift first as necessary) ‘IntermittentOccurrence’ - -1, 1 for non median values ‘SeasonalDifference’ - remove the last lag values from all values ‘SeasonalDifferenceMean’ - remove the average lag values from all ‘SeasonalDifference7’,’12’,’28’ - non-parameterized version of Seasonal ‘CenterLastValue’ - center data around tail of dataset ‘Round’ - round values on inverse or transform ‘Slice’ - use only recent records ‘ClipOutliers’ - remove outliers ‘Discretize’ - bin or round data into groups ‘DatepartRegression’ - move a trend trained on datetime index “ScipyFilter” - filter data (lose information but smoother!) from scipy “HPFilter” - statsmodels hp_filter “STLFilter” - seasonal decompose and keep just one part of decomposition.
transformation_params (dict) – params of transformers {0: {}, 1: {‘model’: ‘Poisson’}, …} pass through dictionary of empty dictionaries to utilize defaults
random_seed (int) – random state passed through where applicable
-
fill_na
(df, window: int = 10)¶ - Parameters
df (pandas.DataFrame) – Datetime Indexed
window (int) – passed through to rolling mean fill technique
- Returns
pandas.DataFrame
-
fit
(df)¶ Apply transformations and return transformer object.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
fit_transform
(df)¶ Directly fit and apply transformations to convert df.
-
inverse_transform
(df, trans_method: str = 'forecast', fillzero: bool = False)¶ Undo the madness.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
trans_method (str) – ‘forecast’ or ‘original’ passed through
fillzero (bool) – if inverse returns NaN, fill with zero
-
classmethod
retrieve_transformer
(transformation: str = None, param: dict = {}, df=None, random_seed: int = 2020)¶ Retrieves a specific transformer object from a string.
- Parameters
df (pandas.DataFrame) – Datetime Indexed - required to set params for some transformers
transformation (str) – name of desired method
param (dict) – dict of kwargs to pass (legacy: an actual param)
- Returns
transformer object
-
transform
(df)¶ Apply transformations to convert df.
-
autots.
RandomTransform
(transformer_list: dict = {None: 0.0, 'MinMaxScaler': 0.05, 'PowerTransformer': 0.02, 'QuantileTransformer': 0.05, 'MaxAbsScaler': 0.05, 'StandardScaler': 0.04, 'RobustScaler': 0.05, 'PCA': 0.01, 'FastICA': 0.01, 'Detrend': 0.1, 'RollingMeanTransformer': 0.02, 'RollingMean100thN': 0.01, 'DifferencedTransformer': 0.1, 'SinTrend': 0.01, 'PctChangeTransformer': 0.01, 'CumSumTransformer': 0.02, 'PositiveShift': 0.02, 'Log': 0.01, 'IntermittentOccurrence': 0.01, 'SeasonalDifference': 0.1, 'cffilter': 0.01, 'bkfilter': 0.05, 'convolution_filter': 0.001, 'HPFilter': 0.02, 'DatepartRegression': 0.01, 'ClipOutliers': 0.05, 'Discretize': 0.03, 'CenterLastValue': 0.01, 'Round': 0.02, 'Slice': 0.02, 'ScipyFilter': 0.02, 'STLFilter': 0.01}, transformer_max_depth: int = 4, na_prob_dict: dict = {'ffill': 0.3, 'fake_date': 0.1, 'rolling_mean': 0.2, 'rolling_mean_24': 0.1, 'IterativeImputer': 0.1, 'mean': 0.05, 'zero': 0.05, 'ffill_mean_biased': 0.1, 'median': 0.05, None: 0.001, 'interpolate': 0.5, 'KNNImputer': 0.05, 'IterativeImputerExtraTrees': 0.0001}, fast_params: bool = None, traditional_order: bool = False)¶ Return a dict of randomly choosen transformation selections.
SinTrend is used as a signal that slow parameters are allowed.
-
autots.
long_to_wide
(df, date_col: str = 'datetime', value_col: str = 'value', id_col: str = 'series_id', aggfunc: str = 'first')¶ Take long data and convert into wide, cleaner data.
- Parameters
df (pd.DataFrame) –
date_col (str) –
value_col (str) –
the name of the column with the values of the time series (ie sales $)
id_col (str) –
name of the id column, unique for each time series
aggfunc (str) –
passed to pd.pivot_table, determines how to aggregate duplicates for series_id and datetime
other options include “mean” and other numpy functions, beware data must already be input as numeric type for these to work. if categorical data is provided, aggfunc=’first’ is recommended
-
autots.
model_forecast
(model_name, model_param_dict, model_transform_dict, df_train, forecast_length: int, frequency: str = 'infer', prediction_interval: float = 0.9, no_negatives: bool = False, constraint: float = None, future_regressor_train=[], future_regressor_forecast=[], holiday_country: str = 'US', startTimeStamps=None, grouping_ids=None, random_seed: int = 2020, verbose: int = 0, n_jobs: int = 'auto', template_cols: list = ['Model', 'ModelParameters', 'TransformationParameters', 'Ensemble'], horizontal_subset: list = None)¶ Takes numeric data, returns numeric forecasts.
Only one model (albeit potentially an ensemble)! Horizontal ensembles can not be nested, other ensemble types can be.
Well, she turned me into a newt. A newt? I got better. -Python
- Parameters
model_name (str) – a string to be direct to the appropriate model, used in ModelMonster
model_param_dict (dict) – dictionary of parameters to be passed into the model.
model_transform_dict (dict) – a dictionary of fillNA and transformation methods to be used pass an empty dictionary if no transformations are desired.
df_train (pandas.DataFrame) – numeric training dataset of DatetimeIndex and series as cols
forecast_length (int) – number of periods to forecast
frequency (str) – str representing frequency alias of time series
prediction_interval (float) – width of errors (note: rarely do the intervals accurately match the % asked for…)
no_negatives (bool) – whether to force all forecasts to be > 0
constraint (float) – when not None, use this value * data st dev above max or below min for constraining forecast values.
future_regressor_train (pd.Series) – with datetime index, of known in advance data, section matching train data
future_regressor_forecast (pd.Series) – with datetime index, of known in advance data, section matching test data
holiday_country (str) – passed through to holiday package, used by a few models as 0/1 regressor.
n_jobs (int) – number of CPUs to use when available.
template_cols (list) – column names of columns used as model template
horizontal_subset (list) – columns of df_train to use for forecast, meant for internal use for horizontal ensembling
- Returns
Prediction from AutoTS model object
- Return type
PredictionObject (autots.PredictionObject)
-
autots.
create_lagged_regressor
(df, forecast_length: int, frequency: str = 'infer', scale: bool = True, summarize: str = None, backfill: str = 'bfill', n_jobs: str = 'auto', fill_na: str = 'ffill')¶ Create a regressor of features lagged by forecast length. Useful to some models that don’t otherwise use such information.
It is recommended that the .head(forecast_length) of both regressor_train and the df for training are dropped. df = df.iloc[forecast_length:]
- Parameters
df (pd.DataFrame) – training data
forecast_length (int) – length of forecasts, to shift data by
frequency (str) – the ever necessary frequency for datetime things. Default ‘infer’
scale (bool) – if True, use the StandardScaler to standardize the features
summarize (str) – options to summarize the features, if large: ‘pca’, ‘median’, ‘mean’, ‘mean+std’, ‘feature_agglomeration’, ‘gaussian_random_projection’, “auto”
backfill (str) – method to deal with the NaNs created by shifting “bfill”- backfill with last values “ETS” -backfill with ETS backwards forecast “DatepartRegression” - backfill with DatepartRegression
fill_na (str) – method to prefill NAs in data, same methods as available elsewhere
- Returns
regressor_train, regressor_forecast