State-of-the-art time series forecasting for PyTorch.

AutonForecasting is a Python library for time series forecasting with deep learning models. It includes benchmark datasets, data-loading utilities, evaluation functions, statistical tests, univariate model benchmarks and SOTA models implemented in PyTorch and PyTorchLightning.

Accuracy:

• Global model is fitted simultaneously for several time series.
• Shared information helps with highly parametrized and flexible models.
• Useful for items/skus that have little to no history available.

Efficiency:

• Automatic featurization processes.
• Fast computations (GPU or TPU).

Required dependencies are included in environment.yml.

• Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS): 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%.

• Exponential Smoothing Recurrent Neural Network (ES-RNN): A hybrid model that combines the expressivity of non linear models to capture the trends while it normalizes using a Holt-Winters inspired model for the levels and seasonals. This model is the winner of the M4 forecasting competition.

• Neural Basis Expansion Analysis (N-BEATS): A model from Element-AI (Yoshua Bengio’s lab) that has proven to achieve state-of-the-art performance on benchmark large scale forecasting datasets like Tourism, M3, and M4. The model is fast to train and has an interpretable configuration.

• Neural Basis Expansion Analysis with Exogenous Variables (N-BEATSx): The neural basis expansion with exogenous variables is an extension to the original N-BEATS that allows it to include time dependent covariates.

• Transformer-Based Models: Transformer-based framework for unsupervised representation learning of multivariate time series.
  • Autoformer: Encoder-decoder model with decomposition capabilities and an approximation to attention based on Fourier transform.
  • Informer: Transformer with MLP based multi-step prediction strategy, that approximates self-attention with sparsity.
  • Transformer: Classical vanilla Transformer.