Darts API Playground

🚧 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