OMIS 2010 Lecture Notes - Lecture 5: Dependent And Independent Variables, Time Series, Exponential Smoothing
20 Aug 2016
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A time-series is a set of observations on a quantitative variable collected over time. In time series analysis, we analyze the past behavior of a variable in order to predict its future behavior. Forecast based only on past values, no other variables are important. Stationary data - a time series variable exhibiting no significant upward or downward trend over time. Nonstationary data - a time series variable exhibiting a significant upward or downward trend over time. Seasonal data - a time series variable exhibiting a repeating patterns at regular intervals over time. Assumes demand in next period is the same as demand in most recent period. Extrapolation models try to account for the past behavior of a time series variable in an effort to predict the future behavior of the variable. We can reduce random variation by smoothing the time series. Two methods to smooth the data are:
