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1 Why the damped trend works Everette S. Gardner, Jr. Eddie McKenzie

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2 Empirical performance of the damped trend “The damped trend can reasonably claim to be a benchmark forecasting method for all others to beat.” (Fildes et al., JORS, 2008) “The damped trend is a well established forecasting method that should improve accuracy in practical applications.” (Armstrong, IJF, 2006)

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3 Why the damped trend works Practice Optimal parameters are often found at the boundaries of the [0, 1] interval. Thus fitting the damped trend is a means of automatic method selection from numerous special cases. Theory The damped trend and each special case has an underlying random coefficient state space (RCSS) model that adapts to changes in trend.

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4 The damped trend method

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5 Special case when ø = 1

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6 Special cases when β = 0

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7 Fit periods for M3 Annual series # YB067

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8 Special cases when β = 0, continued

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9 Special cases when α = β = 0

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10 Fitting the damped trend to the M3 series Multiplicative seasonal adjustment Initial values for level and trend Local: Regression on first 5 observations Global: Regression on all fit data Optimization (Minimum SSE) Parameters only Parameters and initial values (no significant difference from parameters only)

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11 M3 mean symmetric APE (Horizons 1-18) Makridakis & Hibon (2000) with backcasted initial values 13.6% Gardner & McKenzie (2010) with local initial values 13.5 with global initial values 13.8

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12 MethodLocalGlobal Damped trend 43.0% 27.8% Holt SES w/ damped drift SES w/ drift SES RW w/ damped drift RW w/ drift RW0.0 Modified exp. trend Linear trend Simple average Methods identified in the M3 time series Initial values

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13 Initial values Components identified in the M3 time series ComponentLocalGlobal Damped trend 51.3% 36.5% Damped drift Trend Drift Constant level

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14 MethodAnn.Qtr.Mon. Damped trend 25.9% 47.1% 47.5% Holt SES w/ damped drift SES w/ drift SES RW w/ damped drift RW w/ drift RW0.0 Modified exp. trend Linear trend Simple average Methods identified by type of data (Local initial values)

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15 Rationale for the damped trend Brown’s (1963) original thinking: Parameters are constant only within local segments of the time series Parameters often change from one segment to the next Change may be sudden or smooth Such behavior can be captured by a random coefficient state space (RCSS) model There is an underlying RCSS model for the damped trend and each of its special cases

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16 SSOE state space models for the damped trend {A t } are i.i.d. binary random variates White noise innovation processes ε and are different Parameters h and h* are related but usually different

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17 Runs of linear trends in the RCSS model With a strong linear trend, {A t } will consist of long runs of 1s with occasional 0s. With a weak linear trend, {A t } will consist of long runs of 0s with occasional 1s. In between, we get a mixture of models on shorter time scales, i.e. damping.

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18 Advantages of the RCSS model Allows both smooth and sudden changes in trend. is a measure of the persistence of the linear trend. The mean run length is thus and RCSS prediction intervals are much wider than those of constant coefficient models.

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19 Conclusions Fitting the damped trend is actually a means of automatic method selection. There is an underlying RCSS model for the damped trend and each of its special cases. SES with damped drift was frequently identified in the M3 series and should receive some consideration in empirical research.

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20 References Paper and presentation available at:

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