A Gentle Introduction to the Analysis of Sequential Data
Abstract: Sequential data are collected on a manufacturing process by sensors. In order to predict the future process state and to control the process on target, we need to understand the process characteristics as exhibited in the sequential observations and to decompose the process variation to a systematic pattern and a random noise. In this mini-paper, we illustrate some basic concepts, such as autocorrelation and nonstationarity, through examples and demonstrate the ideas behind some simple filtering and smoothing techniques.
Keywords: Time Series Data - Autocorrelation - Stationary - Smoothing - Automatic process control