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.