How to Forecast Sales Using Julius

This article will walk you through one way to accurately predict sales with Julius by training a forecasting model.

Watch our video to learn how to do sales forecasting, step by step

Introduction

Welcome to this comprehensive guide on sales forecasting using Julius. In this tutorial, we will walk you through the process of analyzing sales data, understanding its trends, and making accurate forecasts. Let's dive in!

Step 1: Load Your Sales Data

  • Begin by loading your sales data into Julius. This data must include both dates and sales totals. Several years of sales data is best; at minimum, 2 years is needed.

Step 2: Aggregate and Decompose Data

  • Select the column you wish to focus on. Often, this means aggregate your data and creating a total sales column, which you can do with Julius. You can perform forecasts for different products, but this is best done individually.
  • Perform a time series decomposition to understand the data's underlying patterns. Use the following prompt:
    • Perform time-series decomposition to analyze the data at the weekly level

Step 3: Understand Time Series Decomposition

  • The time series decomposition helps in identifying seasonal trends, the general trend of the data, and any outliers. Having this information in Julius' context will help improve the accuracy of forecasts.

Step 4: Split the Data

  • Divide your data into two groups: 80% for training and 20% for testing. Use the following prompt:
    • Split the data 80:20 train:test
  • The training group will be used to train the forecasting model, while the test group will be used to validate the model's predictions.

Step 5: Train the Forecasting Model

  • Use a SARIMA (Seasonal Autoregressive Integrated Moving Average) model for forecasting. Use the following prompt:
    • Train a SARIMA model to predict weekly sales, taking into consideration the results of the time-series analysis. Note: before training the model, determine the order of the seasonal differencing (D), the order of the seasonal autoregressive term (P), and the order of the seasonal moving average term (Q). Use ACF and PACF plots for this, printing the table data outputs as a step and then reading the data to inform a best guess on the parameters.
  • Providing Julius with the results of the prebvious time series analysis to help it select the best parameters for the forecast.

Step 6: Visualize the Forecast

  • After training the model, prompt Julius to forecast and plot the results against the test data. Use a prompt like the following:
    • Forecast and plot the result against the test data
  • Review the plotted forecast to assess how well the model captures the actual sales trends. You should get a plot that looks like this:
    • Plot of SARIMA forecast result

Step 7: Analyze the Results

  • The visual representation will give you insights into the model's accuracy.
  • Understanding these deviations can help in refining the model or adjusting business strategies.
  • You can also prompt Julius to give you a download link to the forecasted results in a table format.

Conclusion

Sales forecasting is a crucial aspect of business planning, and with tools like Julius, the process becomes democratized. Anyone can now perform quick, accurate sales forecasting. By following the steps outlined in this article, you can harness the power of advanced forecasting models to predict sales trends and make informed business decisions. Whether you're a business owner, a sales analyst, or just curious about data forecasting, this guide offers valuable insights.