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Statistical Forecast Improvements Through Method and Parameter Selections

In using the statistical forecasting solution like the Arkieva Statistical Forecasting Engine, a method that may work well for a forecast situation may not work well for another. To ensure that we’re selecting the right method, it may be necessary to add in options that can handle different forecast situations. These options are called parameters and a forecast method and a forecast may have more than one parameter.

In this webinar, we’ll highlight the Arkieva forecasting approach, and share examples of how the Arkieva forecasting process helps to improve forecast for almost any forecast situation.

Discussion topics include: 

  • The many ways (methods) of measuring the quality of a forecast.

  • How changes to different parameters affect the quality of your forecast.

  • Determining the best forecast method based on your business objective.

  • How to avoid method selection fatigue which may cause you to use out-of-the-box method with default parameters which may not work for specific situation.

  • How the Arkieva forecasting engine determines the set of forecast methods for each forecast situation and selects the right parameters.

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alfred degbotse

About Alfred Degbotse

Alfred Degbotse is a Senior Analytics Consultant at Arkieva. Alfred spent 20 years of his early career with IBM prior to joining Arkieva. While with IBM, he worked in areas of supply planning and business analytics. He also has numerous granted patents in Supply Factory Planning Solutions. Alfred earned his Ph.D. in Operations Research from Virginia Tech.

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