Time Series Forecasting Using Machine Learning

Abstract

Forecasting is an essential part of any business as extensive amount of data is available, one needs to combine statistical model with machine learning to improve accuracy, throughput and overall performance. In this paper a time series forecasting approach is used with machine learning techniques to forecast the store item demands. SARIMA(0,1,1)X(0,1,0)12 model is used with parameters (0,1,0,12) referring to seasonalcomponents of series combined with ARIMA (0,1,1) for trend components. We trained our model taking past 4 year values of store items and predicted sales for next year.