DeepAREstimator Sample

DeepAREstimator_Sample.py

import numpy as np
import pandas as pd

# uncomment to deactivate progress bar
#from gluonts.env import env
#env._push(use_tqdm=False)

from gluonts.dataset.common import ListDataset
from gluonts.dataset.field_names import FieldName
from gluonts.model.deepar import DeepAREstimator
from gluonts.mx.distribution.gaussian import GaussianOutput
from gluonts.mx.trainer import Trainer

def create_dataset(num_series, num_steps, period=24, mu=1, sigma=0.3):
    # create target: noise + pattern
    # noise
    noise = np.random.normal(mu, sigma, size=(num_series, num_steps))

    # pattern - sinusoid with different phase
    # np.tile: Construct an array by repeating A the number of times given by reps
    sin_minumPi_Pi = np.sin(np.tile(np.linspace(-np.pi, np.pi, period), int(num_steps / period)))
    sin_Zero_2Pi = np.sin(np.tile(np.linspace(0, 2 * np.pi, period), int(num_steps / period)))

    pattern = np.concatenate((np.tile(sin_minumPi_Pi.reshape(1, -1),
                                      (int(np.ceil(num_series / 2)),1)),
                              np.tile(sin_Zero_2Pi.reshape(1, -1),
                                      (int(np.floor(num_series / 2)), 1))
                             ),
                             axis=0
                            )

    target = noise + pattern
    return target
    
target = create_dataset(1, 1460*96, 96)
train_ds = ListDataset([{FieldName.TARGET: target[0],
                         FieldName.START: pd.Timestamp("01-01-2016 00:00:00", freq='15T')
                        }],
                      freq='15T')

n = 2
prediction_length=96
estimator = DeepAREstimator(
    prediction_length=prediction_length,
    context_length=2*prediction_length,
    num_cells=100,
    freq="15T",
    distr_output = GaussianOutput(),
    trainer=Trainer(
        ctx='gpu',
        hybridize=True,
        learning_rate=1e-5,
        epochs=n,
        num_batches_per_epoch=1000,
        batch_size=32
    )
)
predictor = estimator.train(train_ds)