• Sinapsis Darts Forecasting (sinapsis-darts-forecasting)

  • CSV Loader
    Load time series data from CSV files into Darts TimeSeries objects.
  • Dataframe Loader
    Convert a pandas DataFrame into a Darts TimeSeries object.
  • Series Loader
    Convert a pandas Series into a Darts TimeSeries object.
  • Darts Transformers
    Apply data transformations (scaling, missing value filling, etc.) using Darts transformer classes.
  • Darts Models
    Fit and predict using Darts baseline, statistical, machine learning, and deep learning models.
  • Time Series Visualization
    Generate Matplotlib images and interactive Plotly HTML charts comparing historical and forecasted values.
  • Time Series Metrics
    Compute Darts metrics (MAE, RMSE, MAPE, etc.) between predicted and ground-truth time series.

• Sinapsis SKTime (sinapsis-sktime)

  • SKTime Forecasters
    Train and generate predictions using sktime forecasting models (ARIMA, AutoARIMA, ExponentialSmoothing, Theta, NaiveForecaster, and more).
  • SKTime Classifiers
    Train and classify time series using sktime classification models (distance-based, dictionary-based, feature-based, deep learning, and dummy classifiers).

• Sinapsis TimesFM (sinapsis-timesfm)

  • TimesFM
    Perform time series forecasting using Google's TimesFM foundation model.

agent:
  name: XGBLSTMForecastingAgent
  description: ''

templates:

- template_name: InputTemplate
  class_name: InputTemplate
  attributes: {}

- template_name: TimeSeriesFromDataframeLoader
  class_name: TimeSeriesFromDataframeLoader
  template_input: InputTemplate
  attributes:
    apply_to: ["content", "past_covariates", "future_covariates"]
    from_pandas_kwargs:
      time_col: "Date"
      fill_missing_dates: True
      freq: "D"

- template_name: MissingValuesFiller
  class_name: MissingValuesFillerWrapper
  template_input: TimeSeriesFromDataframeLoader
  attributes:
    method: "transform"
    missingvaluesfiller_init: {}
    apply_to: ["content", "past_covariates", "future_covariates"]
    transform_kwargs:
      method: "linear"

- template_name: TimeSeries
  class_name: XGBModelWrapper
  template_input: MissingValuesFiller
  attributes:
    forecast_horizon: 100
    xgbmodel_init:
      lags: 30
      lags_past_covariates: 30
      output_chunk_length: 100
      random_state: 42
      n_estimators: 200
      learning_rate: 0.1
      max_depth: 6

To run, simply use:

sinapsis run name_of_the_config.yml

IMPORTANT This docker image depends on the sinapsis-nvidia:base image. Please refer to the official sinapsis instructions to Build with Docker.

1. Build the sinapsis-time-series-forecasting image:

docker compose -f docker/compose.yaml build

2. Start the app container:

docker compose -f docker/compose_apps.yaml up sinapsis-darts-forecasting-gradio -d

3. Check the status:

docker logs -f sinapsis-darts-forecasting-gradio

3. The logs will display the URL to access the webapp, e.g.:

NOTE: The url can be different, check the output of logs

Running on local URL:  http://127.0.0.1:7860

4. To stop the app:

docker compose -f docker/compose_apps.yaml down

To run the webapp using the uv package manager, please:

1. Create the virtual environment and sync the dependencies:

uv sync --frozen

2. Install the wheel:

uv pip install sinapsis-time-series-forecasting[all] --extra-index-url https://pypi.sinapsis.tech

3. Run the webapp:

uv run webapps/darts_time_series_gradio_app.py

4. The terminal will display the URL to access the webapp, e.g.:

NOTE: The url can be different, check the output of the terminal

Running on local URL:  http://127.0.0.1:7860

📙 Documentation

Documentation for this and other sinapsis packages is available on the sinapsis website

Tutorials for different projects within sinapsis are available at sinapsis tutorials page

🔍 License

This project is licensed under the AGPLv3 license, which encourages open collaboration and sharing. For more details, please refer to the LICENSE file.

For commercial use, please refer to our official Sinapsis website for information on obtaining a commercial license.