The main problem of this kind of data is the impossibility of modeling using a linear model because this kind of data includes leptokurtosis, volatility clustering, long memory, volatility smile, and leverage effects. It is used in portfolio optimization, risk management, and asset pricing. Volatility is a vital concept for financial series.

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.Whereas heteroskedasticity is associated with models for cross-section data in textbooks, autoregressive conditional heteroskedasticity (ARCH) can be found in time-series models. The Smarten approach to data discovery is designed as an augmented analytics solution to serve business users. 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. 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. Smarten Augmented Analytics tools include assisted predictive modeling, smart data visualization, self-serve data preparation, Clickless AnalyticsР’В with natural language processing (NLP) for search analytics, Auto Insights, Key Influencer Analytics, and SnapShotР’В monitoring and alerts. 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. 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.Р”Р„RIMAX provides forecasted values of the target variables for user-specified time periods to clearly illustrate results for planning, production, sales and other factors. 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 predictor variables for this use case would be yearly consumer inflation rate, yearly GDP data and yearly population growth rate.Р”В«usiness Benefit:Р’В By analyzing the various combinations of predictor variables, the business can forecast product growth, trends, patterns and seasonality, if any. LetРІР‚в„ўs look at a business use case to illustrate the benefit of the ARIMAX Forecasting method.Р”В«usiness Problem:Р’В A company wants to forecast its product line growth for the new couple of years, based on data from the past thirty (30) years. How Can ARIMAX Forecasting Be Used for Enterprise Analysis? This dataset is suitable for the ARIMAX algorithm because there is more than one variable affecting the GDP РІР‚вЂњ in other words, the dataset is multivariate. As shown in the figure below, the plot of these data points suggests that this is non stationary data with an upward trend. To understand ARIMAX Forecasting, letРІР‚в„ўs look at an example of annual GDP values in India. ARIMAX is suitable for analysis where there are additional explanatory variables (multivariate) in categorical and/or numeric format. 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.Р”Р‡or more information about data trend and pattern analysis techniques, read our article entitled, РІР‚В What Are Data Trends and Patterns, and How Do They Impact Business Decisions?РІР‚в„ўР”Р„RIMAX is related to the ARIMA technique but, while ARIMA is suitable for datasets that are univariate (see the article, entitledРІР‚в„ў What is ARIMA Forecasting and How Can it Be Used for Enterprise Analysis?РІР‚в„ў). This article looks at the ARIMAX Forecasting method of analysis and how it can be used for business analysis.Р”Р„n 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.

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