Lecture For Time Series And Forecasting
TIME SERIES AND
FORECASTING
McGrawHill/Irwin
Copyright © 2010 by The McGrawHill Companies, Inc. All rights reserved.
Time Series and its
Components
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
1.
2.
3.
4.
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
16-2
Secular Trend – Examples
16-3
Cyclical Variation – Sample
Chart
16-4
Seasonal Variation – Sample
Chart
16-5
Linear Trend Plot
16-6
The Moving Average
Method
16-7
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 Linear Trend Plot
16-8
Linear Trend
The long term trend of many business series often approximates a straight line
Linear Trend Equation : Y a bt where :
Y read "Y hat" , is the projected value of the Y variable for a selected value of t a the Y - intercept
(estimated value of Y when t 0) b the slope of the line
(average change in Y for each unit change in t ) t any value of time (coded) that is selected
16-9
Linear Trend
16-10
Use Data Analysis-Regression analysis in
Excel to find the best linear relationship between 2 variables: t and Y
Code time (t) and use it as the independent variable
E.g. let t be 1 for the first year, 2 for the second, and so on (if data are annual)
11
16-11
FORECASTING
McGrawHill/Irwin
Copyright © 2010 by The McGrawHill Companies, Inc. All rights reserved.
Time Series and its
Components
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
1.
2.
3.
4.
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
16-2
Secular Trend – Examples
16-3
Cyclical Variation – Sample
Chart
16-4
Seasonal Variation – Sample
Chart
16-5
Linear Trend Plot
16-6
The Moving Average
Method
16-7
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 Linear Trend Plot
16-8
Linear Trend
The long term trend of many business series often approximates a straight line
Linear Trend Equation : Y a bt where :
Y read "Y hat" , is the projected value of the Y variable for a selected value of t a the Y - intercept
(estimated value of Y when t 0) b the slope of the line
(average change in Y for each unit change in t ) t any value of time (coded) that is selected
16-9
Linear Trend
16-10
Use Data Analysis-Regression analysis in
Excel to find the best linear relationship between 2 variables: t and Y
Code time (t) and use it as the independent variable
E.g. let t be 1 for the first year, 2 for the second, and so on (if data are annual)
11
16-11