lstm-forecaster

Forecast multivariate time series using LSTM or GRU neural networks to model long-range dependencies and nonlinear dynamics. This approach processes multiple input features to predict future values across domains like finance, weather, and energy.

Compare LSTM and GRU models for time series forecasting by running baseline experimentation with a configurable training and evaluation workflow. This allows you to evaluate performance on held-out data to determine which variant better captures your sequence dynamics.

Yes, you can forecast univariate and multivariate sequences in Python using TensorFlow and Keras. These deep learning frameworks support building configurable workflows that preprocess data, train models, and generate long-horizon predictions.

Generate a 50-step time series forecast by training an LSTM or GRU network on your historical data. The configurable workflow preprocesses your data, builds sequences, trains the model, and automatically outputs the requested future horizon.

LSTM time series forecasting works effectively for sensor data and energy consumption by modeling nonlinear patterns. It handles long sequences across various domains, processing multiple input features to produce accurate future value predictions.