Local linear regression for estimating time series data

Abstract

Predicting future performance based on past performance history is a task often undertaken by business process managers. Various statistical and analytical techniques, such as time series and neural network modeling, are available. However, these techniques require the availability of a long time series for the development of a predictive model. Local linear regression (LLR) is an additional nonparametric statistical method that can be used to estimate a time series response variable. The LLR technique does not require a long time series for the development of a predictive model. In fact, the LLR technique can be utilized for prediction once three data points have been collected from the business process. In this work, LLR was evaluated as a tool for predicting future values of process parameters based on historical values. If successful, the LLR technique could be applied in start-up conditions or used as an alternative in some situations to time series modeling. The LLR procedure outperformed traditional time series techniques for the example stationary data sets and had comparable results to the ARIMA model for the example seasonal data set. In addition the LLR technique uses the data that is currently available from a process as its basis for prediction, thus providing a dynamic predictive technique that can continue to function in the presence of process changes.