Presentation on theme: "“The Art of Forecasting”"— Presentation transcript:
1“The Art of Forecasting” Time Series“The Art of Forecasting”1
2Learning Objectives Describe what forecasting is Explain time series & its componentsSmooth a data seriesMoving averageExponential smoothingForecast using trend models Simple Linear Regression Auto-regressiveAs a result of this class, you will be able to...2
3What Is Forecasting? Process of predicting a future event Underlying basis of all business decisionsProductionInventoryPersonnelFacilities4
4Forecasting Approaches Qualitative MethodsQuantitative MethodsUsed when situation is vague & little data existNew productsNew technologyInvolve intuition, experiencee.g., forecasting sales on Internet5
5Forecasting Approaches Qualitative MethodsQuantitative MethodsUsed when situation is vague & little data existNew productsNew technologyInvolve intuition, experiencee.g., forecasting sales on InternetUsed when situation is ‘stable’ & historical data existExisting productsCurrent technologyInvolve mathematical techniquese.g., forecasting sales of color televisions6
6Quantitative Forecasting Select several forecasting methods‘Forecast’ the pastEvaluate forecastsSelect best methodForecast the futureMonitor continuously forecast accuracy7
9Quantitative Forecasting Methods Time SeriesModels10
10Quantitative Forecasting Methods Time SeriesCausalModelsModels11
11Quantitative Forecasting Methods Time SeriesCausalModelsModelsMovingExponentialTrendAverageSmoothingModels12
12Quantitative Forecasting Methods Time SeriesCausalModelsModelsMovingExponentialTrendRegressionAverageSmoothingModels13
13Quantitative Forecasting Methods Time SeriesCausalModelsModelsMovingExponentialTrendRegressionAverageSmoothingModels15
14What is a Time Series? Set of evenly spaced numerical data Obtained by observing response variable at regular time periodsForecast based only on past valuesAssumes that factors influencing past, present, & future will continueExampleYear:Sales:16
15Time Series vs. Cross Sectional Data Time series data is a sequence of observationscollected from a processwith equally spaced periods of time.
16Time Series vs. Cross 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.
17Time Series vs. Cross Sectional Data Time series is dynamic, it does change over time.
18Time Series vs. Cross Sectional Data When working with time series data, it is paramount that the data is plotted so the researcher can view the data.
51Exponential Smoothing Method Form of weighted moving averageWeights decline exponentiallyMost recent data weighted mostRequires smoothing constant (W)Ranges from 0 to 1Subjectively chosenInvolves little record keeping of past data58
82Autoregressive Modeling Used for forecasting trendLike regression modelIndependent variables are lagged response variables Yi-1, Yi-2, Yi-3 etc.Assumes data are correlated with past data values1st Order: Correlated with prior periodEstimate with ordinary least squares105
85Autoregressive Model [An Example] The Office Concept Corp. has acquired a number of office units (in thousands of square feet) over the last 8 years. Develop the 2nd order Autoregressive models.Year Units93 396 297 298 4
86Autoregressive Model [Example Solution] Develop the 2nd order tableUse Excel to run a regression modelYear Yi Yi Yi-2Excel Output
88Quantitative Forecasting Steps Select several forecasting methods‘Forecast’ the pastEvaluate forecastsSelect best methodForecast the futureMonitor continuously forecast accuracy113
89Forecasting Guidelines No pattern or direction in forecast errorei = (Actual Yi - Forecast Yi)Seen in plots of errors over timeSmallest forecast errorMeasured by mean absolute deviationSimplest modelCalled principle of parsimony114
90Pattern of Forecast Error Trend Not Fully Accounted forDesired Pattern115
91Cyclical effects not accounted for Residual AnalysiseeTTRandom errorsCyclical effects not accounted foreeTTTrend not accounted forSeasonal effects not accounted for
92Principal of Parsimony Suppose two or more models provide good fit for dataSelect the Simplest ModelSimplest model types:least-squares linearleast-square quadratic1st order autoregressiveMore complex types:2nd and 3rd order autoregressiveleast-squares exponential
93Summary Described what forecasting is Explained time series & its componentsSmoothed a data seriesMoving averageExponential smoothingForecasted using trend models121
94You and StatGraphics Specification Estimation [Know assumptions underlying various models.]Estimation[Know mechanics of StatGraphics Plus Win].Diagnostic checking