Forecasting industry big data with holt winter’s method from a perspective of in-memory paradigm

Abstract

Industrial data in time series exhibit seasonal behavior like demand for materials for any Industry and this call for seasonal forecasting which is of considerable importance for any planning for an industry as the business profitability revolves around the decisions based on the results of forecasting. This paper tries to explore the situations in the business industry domain which concentrates on the analysis of seasonal time series data using Holt-Winters exponential smoothing methods and along with this exploration the paper tries to optimize most of the intermediate stage for detailed analysis using in-memory database and sql techniques.