How to Train and Download a Forecasting Model from Julius

This post will walk you through methods for training a forecasting model on your data, and then how to download the model for later use.

Watch our video to learn how to train and download a model from Julius, step by step

Introduction

Welcome to this comprehensive guide on leveraging Julius for your data modeling needs. In this tutorial, we will guide you through the process of training a forecasting model using a dataset and then downloading it for future use. Let's get started!

Step 1: Load the Dataset

  • Begin by loading the dataset into Julius and start a conversation.

Step 2: Split the Data

  • Make a train-test split on the first two-thirds of the time series data.
  • This means you'll use the first two-thirds for training and reserve the final third for testing and validation. This split allows you to forecast using the trained model and then compare its predictions against the actual data to assess its accuracy.

Step 3: Train the Model

  • Prompt Julius to train an ARIMA model, a type of forecasting model.
  • Once trained, you'll receive a summary of the model's parameters and performance.

Step 4: Save the Model

  • Save the trained model as a 'pickle', a Python format for serializing objects.
  • Save the test set as ‘test_Set’.
  • Julius will provide download links for both the split training data and the trained model.
  • Download and save these files for future reference or use.

Step 5: Reuse the Model

  • With Julius, all files are removed from the server after an activity for security reasons.
  • If you wish to reuse a trained model, you can save it and then re-upload it in a subsequent session.

Step 6: Forecast and Plot

  • Use the trained ARIMA model to forecast your data.
  • Plot the forecasted values against the actual values from the test set to visualize the model's performance.
  • The plot will help you assess how well the model captures patterns like seasonality.

Step 7: Save the Forecast

  • You can save the forecasted model again, this time as a file for later use.
  • This is especially useful if you're technically inclined and familiar with Python, as it allows you to easily integrate trained models into your own projects.

Conclusion

  • Harnessing the power of Julius, you can streamline the process of training, testing, and reusing forecasting models. Whether you're a data enthusiast or a seasoned professional, this guide offers a straightforward approach to model training and deployment. Here's to your data-driven success!