timeseries (1)
TRANSCRIPT
What is a Time What is a Time Series?Series?
Set of evenly spaced numerical dataObtained by observing response variable at
regular time periods Forecast based only on past values
Assumes that factors influencing past, present, & future will continue
ExampleYear: 19951996199719981999Sales: 78.763.589.7 93.292.1
Time Series vs. Time Series vs. Cross Sectional DataCross Sectional Data Contrary to restrictions placed
on cross-sectional data, the major purpose of forecasting with time series is to extrapolate beyond the range of the explanatory variables.
Time Series Components
Time Series Components
TrendTrend
Time Series Components
TrendTrend CyclicalCyclical
Time Series Components
TrendTrend
SeasonalSeasonal
CyclicalCyclical
Time Series Components
TrendTrend
SeasonalSeasonal
CyclicalCyclical
IrregularIrregular
Trend Component Persistent, overall upward or downward
pattern Due to population, technology etc. Several years duration
Mo., Qtr., Yr.Mo., Qtr., Yr.
ResponseResponse
© 1984-1994 T/Maker Co.
Trend ComponentTrend Component Overall Upward or Downward Movement Data Taken Over a Period of Years
Sales
Time
Upward trend
Cyclical Component Repeating up & down movements Due to interactions of factors influencing
economy Usually 2-10 years duration
Mo., Qtr., Yr.Mo., Qtr., Yr.
ResponseResponseCycle
Cyclical Cyclical ComponentComponent
Upward or Downward Swings May Vary in Length Usually Lasts 2 - 10 Years
Sales
Time
Cycle
Seasonal Component Regular pattern of up & down
fluctuations Due to weather, customs etc. Occurs within one year
Mo., Qtr.Mo., Qtr.
ResponseResponseSummerSummer
© 1984-1994 T/Maker Co.
Seasonal Seasonal ComponentComponent
Upward or Downward Swings Regular Patterns Observed Within One Year
Sales
Time (Monthly or Quarterly)
Winter
Irregular Component Erratic, unsystematic, ‘residual’
fluctuations Due to random variation or unforeseen
eventsUnion strikeWar
Short duration & nonrepeating
© 1984-1994 T/Maker Co.
Random or Irregular Random or Irregular ComponentComponent
Erratic, Nonsystematic, Random, ‘Residual’ Fluctuations
Due to Random Variations of Nature
Accidents
Short Duration and Non-repeating
Time Series Forecasting
Time Series Forecasting
TimeSeries
Time Series Forecasting
TimeSeries
Trend?
Time Series Forecasting
TimeSeries
Trend?SmoothingMethods
No
Time Series Forecasting
TimeSeries
Trend?SmoothingMethods
TrendModels
YesNo
Time Series Forecasting
TimeSeries
Trend?SmoothingMethods
TrendModels
YesNo
ExponentialSmoothing
MovingAverage
Time Series Forecasting
Linear
TimeSeries
Trend?SmoothingMethods
TrendModels
YesNo
ExponentialSmoothing
Quadratic Exponential Auto-Regressive
MovingAverage
Time Series Time Series AnalysisAnalysis
Plotting Time Plotting Time Series DataSeries Data
09/83 07/86 05/89 03/92 01/95
Month/Year
0
2
4
6
8
10
12
Number of Passengers
(X 1000)Intra-Campus Bus Passengers
Data collected by Coop Student (10/6/95)
Time Series Time Series ForecastingForecasting
Linear
TimeSeries
Trend?SmoothingMethods
TrendModels
YesNo
ExponentialSmoothing
Quadratic Exponential Auto-Regressive
MovingAverage
Moving Average Method Series of arithmetic means Used only for smoothing
Provides overall impression of data over time
Moving Average Moving Average MethodMethod Series of arithmetic means Used only for smoothing
Provides overall impression of data over time
Used for elementary forecasting Used for elementary forecasting
Time Series Time Series ForecastingForecasting
Linear
TimeSeries
Trend?SmoothingMethods
TrendModels
YesNo
ExponentialSmoothing
Quadratic Exponential Auto-Regressive
MovingAverage
Exponential Exponential Smoothing MethodSmoothing Method Form of weighted moving average
Weights decline exponentiallyMost recent data weighted most
Requires smoothing constant (W)Ranges from 0 to 1Subjectively chosen
Involves little record keeping of past data
Time Series Plot [Revised]
01/93 01/94 01/95 01/96 01/97
Month/Year
183
185
187
189
191
193
Number of Surgeries
(X 100)
Revised Surgery Data(Time Sequence Plot)
Source: General Hospital, Metropolis
Seasonality Plot
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
99.7
99.9
100.1
100.3
100.5
Monthly Index
Revised Surgery Data(Seasonal Decomposition)
Source: General Hospital, Metropolis
Trend Analysis
12/92 10/93 8/94 6/95 9/96 2/97 12/97
Month/Year
18
18.3
18.6
18.9
19.2
19.5
Number of Surgeries
(X 1000)
Revised Surgery Data(Trend Analysis)
Source: General Hospital, Metropolis
Questions?
ANOVA