--- title: N-HiTS keywords: fastai sidebar: home_sidebar nb_path: "nbs/models_nhits__nhits.ipynb" ---
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A new model for long-horizon forecasting which incorporates novel hierarchical interpolation and multi-rate data sampling techniques to specialize blocks of its architecture to different frequency band of the time-series signal. It achieves SoTA performance on several benchmark datasets, outperforming current Transformer-based models by more than 25%. Paper available at https://arxiv.org/abs/2201.12886

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class NHITS[source]

NHITS(n_time_in:int, n_time_out:int, n_x:int, n_s:int, shared_weights:bool, activation:str, initialization:str, stack_types:List[str], n_blocks:List[int], n_layers:List[int], n_mlp_units:List[List[int]], n_x_hidden:int, n_s_hidden:int, n_pool_kernel_size:List[int], n_freq_downsample:List[int], pooling_mode:str, interpolation_mode:str, batch_normalization:bool, dropout_prob_theta:float, learning_rate:float, lr_decay:float, lr_decay_step_size:int, weight_decay:float, loss_train:str, loss_hypar:float, loss_valid:str, frequency:str, random_seed:int) :: LightningModule

Hooks to be used in LightningModule.

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NHITS.forecast[source]

NHITS.forecast(Y_df:DataFrame, X_df:DataFrame=None, S_df:DataFrame=None, batch_size:int=1, trainer:Trainer=None)

Method for forecasting self.n_time_out periods after last timestamp of Y_df.

Parameters

Y_df: pd.DataFrame Dataframe with target time-series data, needs 'unique_id','ds' and 'y' columns. X_df: pd.DataFrame Dataframe with exogenous time-series data, needs 'unique_id' and 'ds' columns. Note that 'unique_id' and 'ds' must match Y_df plus the forecasting horizon. S_df: pd.DataFrame Dataframe with static data, needs 'unique_id' column. bath_size: int Batch size for forecasting. trainer: pl.Trainer Trainer object for model training and evaluation.

Returns

forecast_df: pd.DataFrame Dataframe with forecasts.

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N-HITS Usage Example

Load Data

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import pandas as pd
from neuralforecast.data.datasets.epf import EPF
from neuralforecast.data.tsloader import TimeSeriesLoader

import pylab as plt
from pylab import rcParams
plt.style.use('seaborn-whitegrid')
plt.rcParams['font.family'] = 'serif'

FONTSIZE = 19

# Load and plot data
Y_df, X_df, S_df = EPF.load_groups(directory='./data', groups=['NP','FR'])

fig = plt.figure(figsize=(15, 6))
plt.plot(Y_df[Y_df['unique_id']=='NP'].ds, Y_df[Y_df['unique_id']=='NP'].y.values, color='#628793', linewidth=0.4)
plt.ylabel('Price [EUR/MWh]', fontsize=19)
plt.xlabel('Date', fontsize=15)
plt.show()
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Declare Model and Data Parameters

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mc = {}
mc['model'] = 'n-hits'
mc['mode'] = 'simple'
mc['activation'] = 'SELU'

mc['n_time_in'] = 24*3
mc['n_time_out'] = 24
mc['n_x_hidden'] = 8
mc['n_s_hidden'] = 0

mc['stack_types'] = ['identity', 'identity', 'identity']
mc['constant_n_blocks'] = 1
mc['constant_n_layers'] = 2
mc['constant_n_mlp_units'] = 256
mc['n_pool_kernel_size'] = [4, 2, 1]
mc['n_freq_downsample'] = [24, 12, 1]
mc['pooling_mode'] = 'max'
mc['interpolation_mode'] = 'linear'
mc['shared_weights'] = False

# Optimization and regularization parameters
mc['initialization'] = 'lecun_normal'
mc['learning_rate'] = 0.001
mc['batch_size'] = 1
mc['n_windows'] = 32
mc['lr_decay'] = 0.5
mc['lr_decay_step_size'] = 2
mc['max_epochs'] = 1
mc['max_steps'] = None
mc['early_stop_patience'] = 20
mc['eval_freq'] = 500
mc['batch_normalization'] = False
mc['dropout_prob_theta'] = 0.0
mc['dropout_prob_exogenous'] = 0.0
mc['weight_decay'] = 0
mc['loss_train'] = 'MAE'
mc['loss_hypar'] = 0.5
mc['loss_valid'] = mc['loss_train']
mc['random_seed'] = 1

# Data Parameters
mc['idx_to_sample_freq'] = 1
mc['val_idx_to_sample_freq'] = 1
mc['n_val_weeks'] = 52
mc['normalizer_y'] = None
mc['normalizer_x'] = 'median'
mc['complete_windows'] = False
mc['frequency'] = 'H'

print(65*'=')
print(pd.Series(mc))
print(65*'=')

mc['n_mlp_units'] = len(mc['stack_types']) * [ mc['constant_n_layers'] * [int(mc['constant_n_mlp_units'])] ]
mc['n_blocks'] =  len(mc['stack_types']) * [ mc['constant_n_blocks'] ]
mc['n_layers'] =  len(mc['stack_types']) * [ mc['constant_n_layers'] ]
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Instantiate Loaders and Model

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from neuralforecast.experiments.utils import create_datasets

train_dataset, val_dataset, test_dataset, scaler_y = create_datasets(mc=mc,
                                                                     S_df=S_df, Y_df=Y_df, X_df=X_df,
                                                                     f_cols=['Exogenous1', 'Exogenous2'],
                                                                     ds_in_val=294*24,
                                                                     ds_in_test=728*24)

train_loader = TimeSeriesLoader(dataset=train_dataset,
                                batch_size=int(mc['batch_size']),
                                n_windows=mc['n_windows'],
                                shuffle=True)

val_loader = TimeSeriesLoader(dataset=val_dataset,
                              batch_size=int(mc['batch_size']),
                              shuffle=False)

test_loader = TimeSeriesLoader(dataset=test_dataset,
                               batch_size=int(mc['batch_size']),
                               shuffle=False)

mc['n_x'], mc['n_s'] = train_dataset.get_n_variables()
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model = NHITS(n_time_in=int(mc['n_time_in']),
              n_time_out=int(mc['n_time_out']),
              n_x=mc['n_x'],
              n_s=mc['n_s'],
              n_s_hidden=int(mc['n_s_hidden']),
              n_x_hidden=int(mc['n_x_hidden']),
              shared_weights=mc['shared_weights'],
              initialization=mc['initialization'],
              activation=mc['activation'],
              stack_types=mc['stack_types'],
              n_blocks=mc['n_blocks'],
              n_layers=mc['n_layers'],
              n_mlp_units=mc['n_mlp_units'],
              n_pool_kernel_size=mc['n_pool_kernel_size'],
              n_freq_downsample=mc['n_freq_downsample'],
              pooling_mode=mc['pooling_mode'],
              interpolation_mode=mc['interpolation_mode'],
              batch_normalization = mc['batch_normalization'],
              dropout_prob_theta=mc['dropout_prob_theta'],
              learning_rate=float(mc['learning_rate']),
              lr_decay=float(mc['lr_decay']),
              lr_decay_step_size=float(mc['lr_decay_step_size']),
              weight_decay=mc['weight_decay'],
              loss_train=mc['loss_train'],
              loss_hypar=float(mc['loss_hypar']),
              loss_valid=mc['loss_valid'],
              frequency=mc['frequency'],
              random_seed=int(mc['random_seed']))
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Train Model

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from pytorch_lightning.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor="val_loss", 
                               min_delta=1e-4, 
                               patience=mc['early_stop_patience'],
                               verbose=False,
                               mode="min")

trainer = pl.Trainer(max_epochs=mc['max_epochs'], 
                     max_steps=mc['max_steps'],
                     gradient_clip_val=1.0,
                     progress_bar_refresh_rate=10, 
                     log_every_n_steps=500, 
                     check_val_every_n_epoch=1,
                     callbacks=[early_stopping])

trainer.fit(model, train_loader, val_loader)
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Make Predictions

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model.return_decomposition = False
outputs = trainer.predict(model, val_loader)

print("outputs[0][0].shape", outputs[0][0].shape)
print("outputs[0][1].shape", outputs[0][1].shape)
print("outputs[0][2].shape", outputs[0][2].shape)
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Forecast

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Y_forecast_df = Y_df[Y_df['ds']<'2016-12-27']
Y_forecast_df.tail()
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X_forecast_df = X_df[X_df['ds']<'2016-12-28']
X_forecast_df.tail()
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model.return_decomposition = False
forecast_df = model.forecast(Y_df=Y_forecast_df, X_df=X_forecast_df, S_df=S_df, batch_size=2)
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forecast_df.head()
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