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Speaker
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Transcript
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This talk is to highlight some
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11.600
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3
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time series platforms. Three are
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from time series platforms and
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34.700
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38.400
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10
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11
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12
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do we need Box Cox
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transformed time series?
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Let's take a look at the data
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00.933
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06.200
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18
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also known as a as an airline
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passenger data set.
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The original series is
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23.900
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23
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model from.
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Why? Let's take a look at a plot...
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43.433
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27
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getting larger. And this series
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cannot be handled by the
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01.666
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31
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in the second picture. So the
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variation does not change
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with the various times series ???
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20.266
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36
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So in the literature people will
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say, well, we will transform
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33.800
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38.700
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43
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of the transform scale, in this case
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here, it's the log scale.
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Sending it to inverse
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04.366
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49
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transform.
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In the past...in the past
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streamline the whole process.
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What you need to do is to put
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39.133
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need to do the models, make
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forecast, then the software,
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61
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will put log passengers into Y,
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but now we don't have to. We
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15.033
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66
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to enter the Box Cox
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transformation parameter Lambda.
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Zero, it means it's a log
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28.333
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31.166
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37.433
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73
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the red triangle menu and click
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either ARIMA or seasonal ARIMA.
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01.766
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78
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12 for seasonal part.
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Without intercept. Click
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22.933
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24.500
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28.733
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85
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86
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forecast taking care of the
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inverse transformation. The
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49.600
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91
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will will show in this.
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plot and the forecast had
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05.033
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08.800
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11.100
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17.866
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98
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models is a workhorse in time
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series forecast platform.
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They can fit and forecast a lot
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performance is somehow comparable
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to the forecasting
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110
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111
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study why it...why this type
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of model works and why some some
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18.600
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115
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116
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type model into the time series
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platform which is designed to
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43.900
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120
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a function of the unknown
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state, unobserved state. Here at
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01.000
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variables and the error term
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by either additive operations
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state is the level state time
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series. Trend state forms a
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134
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135
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state, and also one of the
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previous seasonal states. And
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03.133
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140
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previous trend state will
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tremd to the next.
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trend state and the level state
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point to another time point. And
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there are more arrows...that there
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are more states transitions than is
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series into Y and click OK.
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To fit this type of model, we
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12.300
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16.766
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160
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161
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set, I'm going to enter 12
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for period.
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And I'm going to click Select
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Recommended button.
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From the additive error models and
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47.666
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169
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this particular set, I'm going
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to click constraint parameters
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05.966
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173
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recommended models to fit these
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type of...these time series and
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27.966
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179
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180
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model with smaller AIC and
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my eyes are on the first two
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184
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models. And let me
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overlay the forecast
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17.666
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from the original time series
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more nicely.
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So in my preference, I would
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45.133
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difference? Let me open the
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first one MAA...MAM.
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Let's go down below. This
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10.666
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204
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this one
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component states.
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This is a special for this
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the first letter.
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And the trend is additive by
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second part of this report are
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the...are the state component
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06.100
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part is the prediction of
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this specific state.
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The period of the time series
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has an increasing pattern in
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the past. It keeps increasing
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series and the pattern continues
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toward the future, and this
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03.466
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231
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observed, but the forecast is
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flat. This bothered me.
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Now let's look at second
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state component graph. Level is
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increasing in the past had
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future. This is more reasonable
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plot that I can accept. So is it
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252
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on to the second slide.
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This slide and then the next
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45.100
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259
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on interpreting the forecasts
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from from this model...this type of
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models. Here I would like
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01.933
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07.233
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265
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up. I listed half of them here.
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Oh, nearly half. So let's focus
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30.800
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some increase trend and will
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taper off towards the end.
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And on the other hand, we can
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54.233
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see from the forecast using this
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type model. If seasonality
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is not involved. When I
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14.200
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19.566
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the first one, this is
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a flat forecast if the seasonality
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51.266
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have a linear increase
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patern and so on so forth
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similar to the others. Now
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06.800
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12.566
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it's merely increasing.
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After applying the
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the multiplicative seasonality on
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the top of our increasing
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this type...different type of
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shapes
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flat
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10.733
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we get those different...different
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shapes. So I I re entering ???
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33.466
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40.433
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what we eventually see in
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the forecast.
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You have the flat patterns or
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00.733
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323
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parameters. So I separated
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these parts and also I
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20.400
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trend will usually look flat,
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we will get an increase
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pattern in the level state.
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When it's linear and
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when it's curved.
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It's all depends on how this
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57.666
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339
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increasing or decreasing in the
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level exponentially. So this is
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13.466
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is lean and think of it as
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compound interest rate if
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if the level state increase
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32.466
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they make forecasts, they try to...
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try not to overshoot
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or undershoot the forecast that
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59.833
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355
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how to interpret
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the forecast from state
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20.133
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359
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second one, none of of these
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models are stationary. They are
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37.466
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364
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So if you are considering these
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time series. Things
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55.566
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59.600
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369
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third one, if you just see
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that time series not
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12.000
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a result in a...in
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the next slide that will fit
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31.933
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378
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compare across type of
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model be careful.
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This slide is to show how...
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51.800
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383
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is the forecast.
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And similarly, I plot my
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07.400
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387
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apply these type of state space
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smoothing models to stationary
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time series? Here I simulate a
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32.633
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393
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394
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models to this time series, the
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best model turns out to be in an
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54.266
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399
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400
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rather different becauses it is
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a random walk model and the
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16.466
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18.733
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22.333
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408
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feature in this presentation
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forecast on holdback.
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This feature allows you
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37.966
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40.500
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45.833
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415
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417
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one is from another model.
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And then you can compare these
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11.833
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423
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424
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to activate this feature. Then I
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need to specify
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the length of the holdback
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35.200
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38.466
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430
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431
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432
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click Select Recommended,
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and check Constraint
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06.433
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438
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439
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portion of the series,
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we listed the holdback
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26.000
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443
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444
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by default, but you can
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always change the metrics you
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39.900
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44.866
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449
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reports are similar to
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to that got from the analysis
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results without activating this
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05.433
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454
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455
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let's let's let me summarize
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what we have learned from
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25.566
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459
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performance over the holdback
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data. But those criterias are
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00
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41.366
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463
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process. We see the rather
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different from how we use
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57.700
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467
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part of the model fitting
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process, so this is something
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17.000
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471
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holdback to evaluate
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different models based on their
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forecasting performance. So we
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32.033
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35.366
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478
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column is that time series
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indicator. Y is time series
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00
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55.666
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482
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summarize the data set, either
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time or time series, by
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00
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09.533
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12.066
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16.600
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488
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489
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specification or change the
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model selection strategy, we
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00
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39.200
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494
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check...change selection in the
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first combo box to forecasting
|
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performance. Then we can choose
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forecasting performance
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00
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04.633
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500
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we want forecast. But you can
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choose any...change to any
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17.833
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25.466
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30.866
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506
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507
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using the training
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time series, select the best
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00
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49.800
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52.333
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54.766
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57.900
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00.966
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515
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series platform. First analyze
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Box Cox Transformed time series.
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The second one is fit state
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00
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27.666
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521
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522
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as well and using it as
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And model selection method.
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Thank you very much.
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