11a55an / lstnet-tensorflow

TensorFlow implementation of LSTNet model for multivariate time series forecasting.

LSTNet TensorFlow

TensorFlow implementation of multivariate time series forecasting model introduced in Lai, G., Chang, W. C., Yang, Y., and Liu, H. (2018). Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR '18). Association for Computing Machinery, New York, NY, USA, 95–104.

Dependencies

pandas==1.5.2
numpy==1.23.5
tensorflow==2.11.0
plotly==5.11.0
kaleido==0.2.1

Usage

import numpy as np

from lstnet_tensorflow.model import LSTNet
from lstnet_tensorflow.plots import plot

# Generate some time series
N = 500
t = np.linspace(0, 1, N)
e = np.random.multivariate_normal(mean=np.zeros(3), cov=np.eye(3), size=N)
a = 10 + 10 * t + 10 * np.cos(2 * np.pi * (10 * t - 0.5)) + 1 * e[:, 0]
b = 20 + 20 * t + 20 * np.cos(2 * np.pi * (20 * t - 0.5)) + 2 * e[:, 1]
c = 30 + 30 * t + 30 * np.cos(2 * np.pi * (30 * t - 0.5)) + 3 * e[:, 2]
y = np.hstack([a.reshape(-1, 1), b.reshape(-1, 1), c.reshape(-1, 1)])

# Fit the model
model = LSTNet(
    y=y,
    forecast_period=100,
    lookback_period=200,
    kernel_size=3,
    filters=4,
    gru_units=4,
    skip_gru_units=3,
    skip=50,
    lags=100,
)

model.fit(
    loss='mse',
    learning_rate=0.01,
    batch_size=64,
    epochs=100,
    verbose=1
)

# Generate the forecasts
df = model.forecast(y=y)

# Plot the forecasts
fig = plot(df=df)
fig.write_image('results.png', scale=4, height=900, width=700)