Forecasting industry big data with Holt Winter's method from a perspective of in-memory paradigm
SS Dasgupta, P Mahanta, R Roy… - On the Move to …, 2014 - Springer
SS Dasgupta, P Mahanta, R Roy, G Subramanian
On the Move to Meaningful Internet Systems: OTM 2014 Workshops: Confederated …, 2014•SpringerIndustrial 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 …
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 …
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.
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