Are you capturing the full value of your data and can you afford to only have univariate solutions in a multivariate world?

Are you getting the most out of your forecasts?

We’ve seen that it’s pretty commonplace for analysts to employ time-series models when forecasting. Nothing wrong in theory to go ahead with that but at times, do you get a sense that you might not be getting the most out of your forecasts by depending solely on these univariate models?

While time-series models like Arima have traditionally been a go-to method for forecasting, it is now widely recognized that relying solely on this approach is insufficient for staying ahead of the competition.
Simply put, relying on the old standby method of Arima just isn't cutting it anymore.

Univariate time-series models - traditionally assume that the underlying statistical properties of your data remain constant over time. This is not reflective of reality. 

Numerous case studies have shown that companies that have implemented a more comprehensive forecasting strategy have seen significant benefits.

For example, a retail company that utilized both Arima and automated forecasting software was able to accurately predict demand for seasonal products and stock inventory accordingly. This led to a significant increase in sales and customer satisfaction, as customers were able to purchase desired products without the frustration of items being out of stock.

Here's another case. A manufacturing company was struggling with forecasting due to the complexity of its product lines. By incorporating automated forecasting software into their strategy, they were able to account for a more significant number of variables, even employ hierarchical forecasting to align their forecasting results, and achieve much more accurate results.

This allowed them to optimize their production processes and improve their bottom line.

"Time-series models may not be able to capture such dynamics accurately, leading to inaccurate forecasts.

What this implies is you might be missing out on capturing short-term fluctuations or trends, and even changes in the relationships between different variables that might have an impact on your forecast."

Shortcomings of only relying on time-series models

Inability to capture real-world dynamics

‍Quite a bit of time-series data exhibits non-stationarity, such as trends, seasonal patterns, or sudden shifts in the mean or variance. ARIMA models may not be able to capture such dynamics accurately, leading to inaccurate forecasts. What this implies is you might be missing out on capturing short-term fluctuations or trends, and even changes in the relationships between different variables that might have an impact on your forecast.

Only considers the historical values of a single variable.
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‍We live in a complex, dynamic environment. Given that data can be influenced by multiple variables or external macroeconomic factors - how relevant is it to take only the standalone historical value of a variable into account?

That’s one of the limitations of depending on only univariate models when forecasting. Failing to take multiple variables into consideration could mean generating incomplete or biased forecasts as you only stand to have an incomplete picture of what the relevant market drivers are, and what it implies for your forecast.

This is a good reason to consider expanding your model scope. If you were to take it one step further and bring multivariate models into the equation, it could potentially make a world of a difference in your forecasting results. 

By analyzing the interdependencies between different variables, multivariate models capture the underlying structure of the data more accurately, and tend to provide more reliable forecasts. They should not be the sole approach. Incorporating other techniques, such as machine learning or Bayesian methods, and regularly updating the model with new data can help capture the full value of the data and improve the accuracy of the forecast.

Now, let’s dive into what you’ll get if you incorporate the use of multivariate models. 
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Why incorporate the use of multivariate models in forecasting?

Get improved forecast accuracy

Given that multivariate models can handle more data, they’re optimized to consider multiple variables, as compared to univariate models like Arima which typically only consider one predictor variable. By including multiple predictors, multivariate models can control for confounding variables that may affect the relationship between the independent and dependent variables.

This increases the accuracy and precision of the estimates obtained from the model. Being more equipped to capture changes in the relationship between the variables and the outcome, paves the way for more accurate and robust forecasts.

Generate better, useful insights

The advantage of multivariate models is their ability to handle multiple variables. You’ve got your sales data, internal data, and intelligence on your specific industry, and you want to also be able to weave in external factors.

The advantage of multivariate models is their ability to handle multiple variables, and identify your meaningful key market drivers. By examining multiple variables simultaneously, multivariate models can capture complex interactions and relationships that might otherwise be missed by univariate models. 

Here’s another example. A univariate model might suggest a positive relationship between two variables, but the multivariate model’s strength lies in revealing that this relationship might only be true for a certain subset of the population, or identifying that the relationship is actually negative when other variables are taken into account.

Expect improved robustness

Why is this important? Multivariate models are often more robust than univariate models; they're primed to handle outliers, missing data, and any other challenges in your data. Even in the presence of noisy or incomplete data. Indicio does this in one click.

Did you know that multivariate models also has the capacity to handle missing data more effectively than univariate models? By incorporating information from other variables, these models can use the available data more efficiently and provide more accurate estimates.

That said, we wouldn’t be doing you any favors if we didn’t point out the “necessary work” surrounding the shift from univariate to multivariate models. 

However, here are some considerations to keep in mind

How much data do you have on hand?

Multivariate models generally require more data than univariate models, no surprise there. Obtaining data for all the variables can also be a challenge. Compounding this, the data may be incomplete, inconsistent or unavailable.

In Indicio, we’ve got a plethora of supported data vendors in our library - this means if you’re looking for commodities data or the CPI in Refinitiv, or Producer Price Indexes (PPI) data in FRED, you can easily include this data point alongside other internal data or market-leading indicators in your forecast. 

How good is your data?

For multivariate models to work their magic, sub par data quality is a no-no. If anything, a hurdle. The accuracy of the forecast is dependent on you having data that is free from errors, outliers, and missing values.

In under 5 minutes, Indicio automatically processes your imported data, testing for seasonality, missing values, and outliers. If any of these are detected, you can choose to adjust the data as appropriate.

Time to compute = Very high. How much time do you have?

For multivariate models to work their magic, sub par data quality is a no-no. If anything, a hurdle. The accuracy of the forecast is dependent on you having data that is free from errors, outliers, and missing values.

In under 5 minutes, Indicio automatically processes your imported data, testing for seasonality, missing values, and outliers. If any of these are detected, you can choose to adjust the data as appropriate.

Multivariate models can be notoriously time-consuming to build, in comparison to univariate models. And for good reason. There’s some key reasons for this. Let’s go into the first factor:

Making sense of the complexity and achieving high explainability

Multivariate models can be more challenging to interpret than univariate models because they involve multiple variables that interact in complex ways. How do you explain the results of your models to non-technical audiences?  How do you get around explaining to your stakeholders that it is exactly the oil prices that had an impact on sales? What’s the best way to achieve high explainability? 

If you’ve heard of Shapley values, you probably have an idea of how it can be a great tool to substantiate your forecasted numbers - the why behind the forecasted outcome, essentially the contribution of each variable towards the forecasted value - at any given point in time.

By using the integrated Shapley additive values in Indicio, you can better articulate and justify your forecasted numbers to your stakeholders.

The million-dollar question

While univariate time-series models have traditionally been the go-to method for forecasting, relying solely on them may not be enough to stay ahead of the competition, or even be sufficient in generating results that you can trust.

The case for incorporating multivariate models stands strong, notwithstanding the fact that multivariate models generally do require more data, and obtaining data for all the variables can be a challenge.

When you have the data in place (through internal or external sources), having those models in place pays for itself a thousandfold.

The million-dollar question is: Are you capturing the full value of your data and can you afford to only have univariate solutions in a multivariate world?