NeuralForecast offers a large collection of neural forecasting models focusing on their performance, usability, and robustness. The models range from classic networks like RNNs to the latest transformers: MLP, LSTM, GRU, RNN, TCN, TimesNet, BiTCN, DeepAR, NBEATS, NBEATSx, NHITS, TiDE, DeepNPTS, TSMixer, TSMixerx, MLPMultivariate, DLinear, NLinear, TFT, Informer, AutoFormer, FedFormer, PatchTST, iTransformer, StemGNN, and TimeLLM. There is a shared belief in Neural forecasting methods' capacity to improve forecasting pipeline's accuracy and efficiency. Unfortunately, available implementations and published research are yet to realize neural networks' potential. They are hard to use and continuously fail to improve over statistical methods while being computationally prohibitive. For this reason, we created NeuralForecast, a library favoring proven accurate and efficient models focusing on their usability.
Features
- Fast and accurate implementations of more than 30 state-of-the-art models
- Support for exogenous variables and static covariates
- Interpretability methods for trend, seasonality and exogenous components
- Probabilistic Forecasting with adapters for quantile losses and parametric distributions
- Train and Evaluation Losses with scale-dependent, percentage and scale independent errors, and parametric likelihoods
- Automatic Model Selection with distributed automatic hyperparameter tuning
- Familiar sklearn syntax: .fit and .predict
Project Samples
