Note

The ARIMA model is particularly well-suited for forecasting time series data with no strong seasonal patterns.

ARIMA Modeling

Internally, we are using the pmdarima library to implement the ARIMA model. This library provides a robust and efficient way to estimate the best ARIMA configuration for a given dataset.The pmdarima library uses a combination of statistical tests and optimization algorithms to automatically select the best ARIMA configuration for the dataset.

This includes determining the optimal values for the parameters p, d, and q, which define the autoregressive, differencing, and moving average components of the model, respectively.

By default, the model will use the ideal parameters that are automatically selected by the pmdarima library. These parameters are chosen based on the characteristics of the dataset and are designed to provide the best possible fit to the data.

Once the optimal ARIMA configuration is determined, the model uses this configuration to predict the next 7 days of data. The predicted values are based on the parameters of the model and are generated using the pmdarima library’s forecasting functionality.

Training and Forecasting with ARIMA

  1. Load the stationarized dataset obtained either directly or by stationarizing it using the Stationarize tab in the Choose or Upload Data section.

  2. Click on the Start Forecasting button to train the data on the ARIMA model.

  3. After clicking on Start Forecasting, the model will be trained on the dataset and a forecast will be generated for the next 7 days.

  4. A graph will be generated showing the history of the training dataset, the test dataset, and the predicted values for the next 7 days.

  5. The predicted values for the next 7 days will be displayed in the form of metrics, along with the Root Mean Squared Error (RMSE) value, which will be printed as a metric.