Chapter 16 - Time series and forecasting. When you have completed this chapter, you will be able to: Define the components of a time series, compute moving average, determine a linear trend equation, compute a trend equation for a nonlinear trend, use a trend equation to forecast future time periods and to develop seasonally adjusted forecasts, determine and interpret a set of seasonal indexes, deseasonalize data using a seasonal index, test for autocorrelation. | Time Series and Forecasting Chapter 16 Goals Define the components of a time series Compute moving average Determine a linear trend equation Compute a trend equation for a nonlinear trend Use a trend equation to forecast future time periods and to develop seasonally adjusted forecasts Determine and interpret a set of seasonal indexes Deseasonalize data using a seasonal index Test for autocorrelation TIME SERIES is a collection of data recorded over a period of time (weekly, monthly, quarterly), an analysis of history, that can be used by management to make current decisions and plans based on long-term forecasting. It usually assumes past pattern to continue into the future Components of a Time Series Secular Trend – the smooth long term direction of a time series Cyclical Variation – the rise and fall of a time series over periods longer than one year Seasonal Variation – Patterns of change in a time series within a year which tends to repeat each year Irregular Variation – . | Time Series and Forecasting Chapter 16 Goals Define the components of a time series Compute moving average Determine a linear trend equation Compute a trend equation for a nonlinear trend Use a trend equation to forecast future time periods and to develop seasonally adjusted forecasts Determine and interpret a set of seasonal indexes Deseasonalize data using a seasonal index Test for autocorrelation TIME SERIES is a collection of data recorded over a period of time (weekly, monthly, quarterly), an analysis of history, that can be used by management to make current decisions and plans based on long-term forecasting. It usually assumes past pattern to continue into the future Components of a Time Series Secular Trend – the smooth long term direction of a time series Cyclical Variation – the rise and fall of a time series over periods longer than one year Seasonal Variation – Patterns of change in a time series within a year which tends to repeat each year Irregular Variation – classified into: Episodic – unpredictable but identifiable Residual – also called chance fluctuation and unidentifiable Time Series and its Components The Moving Average Method Useful in smoothing time series to see its trend Basic method used in measuring seasonal fluctuation Applicable when time series follows fairly linear trend that have definite rhythmic pattern Weighted Moving Average A simple moving average assigns the same weight to each observation in averaging Weighted moving average assigns different weights to each observation Most recent observation receives the most weight, and the weight decreases for older data values In either case, the sum of the weights = 1 Cedar Fair operates seven amusement parks and five separately gated water parks. Its combined attendance (in thousands) for the last 12 years is given in the following table. A partner asks you to study the trend in attendance. Compute a three-year moving average and a three-year weighted moving average .
