This article looks at the ARIMAX Forecasting method of analysis and how it can be used for business analysis.

What is ARIMAX Forecasting?

An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. This method is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.

For more information about data trend and pattern analysis techniques, read our article entitled, ‘ What Are Data Trends and Patterns, and How Do They Impact Business Decisions?’

ARIMAX is related to the ARIMA technique but, while ARIMA is suitable for datasets that are univariate (see the article, entitled’ What is ARIMA Forecasting and How Can it Be Used for Enterprise Analysis?’). ARIMAX is suitable for analysis where there are additional explanatory variables (multivariate) in categorical and/or numeric format.

To understand ARIMAX Forecasting, let’s look at an example of annual GDP values in India. As shown in the figure below, the plot of these data points suggests that this is non stationary data with an upward trend. This dataset is suitable for the ARIMAX algorithm because there is more than one variable affecting the GDP – in other words, the dataset is multivariate.

ARIMAX Forecasting Example

How Can ARIMAX Forecasting Be Used for Enterprise Analysis?

Let’s look at a business use case to illustrate the benefit of the ARIMAX Forecasting method.

Business Problem: A company wants to forecast its product line growth for the new couple of years, based on data from the past thirty (30) years. The predictor variables for this use case would be yearly consumer inflation rate, yearly GDP data and yearly population growth rate.

Business Benefit: By analyzing the various combinations of predictor variables, the business can forecast product growth, trends, patterns and seasonality, if any. The enterprise can also identify any gap between the targeted and estimated growth and develop an appropriate strategy to reduce this gap in order to achieve targets and results.

The ARIMAX forecasting method is suitable for forecasting when the enterprise wishes to forecast data that is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.

ARIMAX provides forecasted values of the target variables for user-specified time periods to clearly illustrate results for planning, production, sales and other factors.

About Smarten

The Smarten approach to augmented analytics and modern business intelligence focuses on the business user and provides tools for Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include assisted predictive modeling, smart data visualization, self-serve data preparation, Clickless Analytics with natural language processing (NLP) for search analytics, Auto Insights, Key Influencer Analytics, and SnapShot monitoring and alerts. These tools are designed for business users with average skills and require no specialized knowledge of statistical analysis or support from IT or data scientists. Businesses can advance Citizen Data Scientist initiatives with in-person and online workshops and self-paced eLearning courses designed to introduce users and businesses to the concept, illustrate the benefits and provide introductory training on analytical concepts and the Citizen Data Scientist role.

The Smarten approach to data discovery is designed as an augmented analytics solution to serve business users. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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