Want to improve your forecasting performance? Here’s why you should consider using multivariate models

We’ve had customers that consistently used GDP as an indicator to watch, and typically looks at how it correlates with their main variable. But something happens three to four months down the road.

They find that GDP, in this case, is no longer correlated to their main variable, and are at a loss on how to proceed.

Typically, we advise against depending on correlation to identify the leading indicators that have an impact on your forecasted variable. We go into the nitty-gritty on why it isn't recommended.

How can incorporating multivariate models improve your forecasting?

• Multivariate models consider multiple variables or inputs simultaneously, allowing for a more comprehensive understanding of the relationships and interactions between different leading indicators affecting the forecast.?
• As these models can capture more complexity and nuances of the data, and better identify the indicators that have the most significant impact on the forecasted figures, this tends to lead to more accurate forecasts.
• They are typically more effective in identifying anomalies and outliers in the data, which can be crucial for detecting errors or unusual events.?

However, building and testing these models can arguably be time-consuming.

It can take anywhere from a few hours to a few weeks to build and test multiple multivariate forecasting models. To compound that, the time taken will depend on the complexity of the models. More complex models will invariably take longer to build. If your data requires cleaning (to detect outliers or seasonality), it can also extend the time taken to build the models.

Being able to generate these multivariate models in a short period of time with a forecasting tool saves you time and resources. Time that can be spent on interpreting the results and making decisions instead of getting bogged down in the model creation process.

Indicio cleans your data in seconds, builds & backtests 30+ models, and performs model averaging, which significantly speeds up the analysis.?

Here's how it's done.

Once you’re up and running with multivariate models, it's wise to also monitor your forecast performance consistently.

This is especially important if there are changes in the factors that affect demand as you might need to relook the relevancy of your leading indicators. You’d perhaps want to introduce and test the impact of new market drivers on your main variable.

By comparing your forecasts to actual results on a regular basis, you can track the accuracy of your forecasts, identify any new relevant indicators. and make the necessary adjustments.?

So how should you evaluate the accuracy of multivariate forecast models?

You can easily compare the multivariate model's performance with your past forecast results to ensure it adds value.

MAPE as a measurement - If the company is predicting values like production volumes, inventory levels, or sales forecasts, a relative error measure like MAPE might be preferred because it provides an intuitive percentage-based error that can be easily understood by stakeholders.

RMSE as a measurement - If the company is working on predictions where scale and magnitude are critical, such as the precise quantity of a material required, RMSE might be more appropriate since it gives an absolute measure of error.

Interested in improving how you forecast? Check out our other insights.

Detect market trend shifts months earlier with Indicio.

From identifying the relevant leading indicators to building models, Indicio enables you to generate highly-accurate forecasts in minutes.

Forecast demand and sales confidently, and stay prepared for any shifts in the market.

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