🎊 Features
- Exogenous Variables: Static, historic and future exogenous support.
- Forecast Interpretability: Plot trend, seasonality and exogenous
NBEATS
,NHITS
,TFT
,ESRNN
prediction components. - Probabilistic Forecasting: Simple model adapters for quantile losses and parametric distributions.
- Train and Evaluation Losses Scale-dependent, percentage and scale independent errors, and parametric likelihoods.
- Automatic Model Selection Parallelized automatic hyperparameter tuning, that efficiently searches best validation configuration.
- Simple Interface Unified SKLearn Interface for
StatsForecast
andMLForecast
compatibility. - Model Collection: Out of the box implementation of
MLP
,LSTM
,RNN
,TCN
,DilatedRNN
,NBEATS
,NHITS
,ESRNN
,Informer
,TFT
,PatchTST
,VanillaTransformer
,StemGNN
andHINT
. See the entire collection here.
Why?
There is a shared belief in Neural forecasting methods’ capacity to improve our 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 createdNeuralForecast
,
a library favoring proven accurate and efficient models focusing on
their usability.
💻 Installation
PyPI
You can installNeuralForecast
’s
released version from the Python package index
pip with:
Conda
Also you can installNeuralForecast
’s
released version from
conda with:
Dev Mode
If you want to make some modifications to the code and see the effects in real time (without reinstalling), follow the steps below:How to Use
