Skip to content

Latest commit

 

History

History
65 lines (50 loc) · 2 KB

README.md

File metadata and controls

65 lines (50 loc) · 2 KB

Darts API Playground

Darts and Streamlit App

🚧 Under Development.

⚠️ Not all models compiled in cloud. Tested list below

A playground web app for the Darts API with Streamlit!

Featuring:

  • Example datasets
  • Upload your own dataset
  • Model training tuning
  • Model forecasting and plotting controls
  • Dataset Seasonality, Trend, and other Metrics
  • Error Metrics over forecasted periods
  • Historical Forecasting
  • Backtested Error Metrics
  • Flexible forecasting horizon and stride for backtesting
  • Downloadable forecasts and data

Explore A Time Series!

Use your own csv data that has a well formed time series and plot some forecasts!

Or use one of the example Darts datasets

Explorable Models

  • NaiveDrift
  • NaiveMean
  • NaiveSeasonal
  • ARIMA
  • VARIMA (Requires Multivariate dataset)
  • ExponentialSmoothing
  • LinearRegressionModel (Hand set Lag)
  • FFT
  • Theta
  • FourTheta
  • KalmanForecaster
  • LightGBMModel
  • RandomForest (Hand set Lag)
  • RegressionModel

Not Yet Explorable Models

  • Ensembles
    • NaiveEnsembleModel
    • EnsembleModel
    • RegressionEnsembleModel
  • Neural Net Based
    • RNNModel (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version,True,True,True,True,False,True,DeepAR paper
    • BlockRNNModel (incl. LSTM and GRU),True,True,True,True,True,False,
    • NBEATSModel,True,True,True,True,True,False,N-BEATS paper
    • TCNModel,True,True,True,True,True,False,"TCN paper, DeepTCN paper, blog post"
    • TransformerModel,True,True,True,True,True,False,
    • TFTModel (Temporal Fusion Transformer),True,True,True,True,True,True,"TFT paper, PyTorch Forecasting"
    • Prophet

More

Cast to np.float32 to slightly speedup the training