Comparative analysis of the outcomes of differing time series forecasting strategies

Time series forecasting of data from various domains has become an increasingly interesting research subject in recent times. Prediction of future sample values is the main goal of time series forecasting. There are mainly two classes of time series forecasting, namely, single step and multi-step forecasting. There are various machine learning approaches that can be used along with various forecasting strategies which exist, some of which can be utilized in and by time series forecasting. However, inappropriate use of strategies can limit the applicability of the techniques to real world problems. This paper provides a review of time series forecasting, a classification of time series forecasting and also approaches and strategies of time series forecasting. It also demonstrates how inappropriate use of point to point rolling forecast strategy for forecasting could lead to unrealistic outcomes and how a multiple step forecasting strategy called Direct H step strategy could help to overcome this issue. Comparative analysis of two strategies using AutoRegressive Integrated Moving Average (ARIMA) approach are demonstrated.