Ensemble of time series forecasting in complex structure

Joint Event on 5^th World Machine Learning and Deep Learning Congress and World Congress on Computer Science, Machine Learning and Big Data

August 30-31, 2018 Dubai, UAE

Balasubramanyam Pisupati and Shanu Agrawal

Robert Bosch Engineering and Business Solutions Limited, India

Posters & Accepted Abstracts: J Comput Sci Syst Biol

Abstract :

Forecasting is necessary for better business understanding and decision making. When it is done manually, it requires a lot of effort and time from multiple departments like logistics, sales, finance, etc. It also involves lot of gut feeling from experienced people and sometimes it might lead to error prone prediction if person is inexperienced or is not aware of past behavior. It is even very challenging for any data scientist to find a forecast model that performs best for all scenarios and in all forecast horizons. In this paper an approach for forecasting using ensemble model is discussed. Ensembling is done using symmetric mean absolute percentage error and mean absolute percentage error calculated from rolling forecast approach. For validation of forecast model, M3 competition data is used. This approach has resulted in better performance on out of sample prediction.

Biography :

Balasubramanyam Pisupati is currently working with Robert Bosch Engineering Solution and Business Solution as a Senior Manager in Data Analytics team. He has accomplished senior statistical professional with rich experience of more than 10 years in software industry related to product development, testing and data mining.

E-mail: Balasubramanyam.Pisupati@in.bosch.com