ARIMA forecasts are way off
0
I am using ARIMA (auto.arima) to forecast for 52 weeks. The time series model fits the data well (see plot below, red line is the fitted value). The input data has a decreasing trend.
The forecasts (highlighted area) however seems to just taking off after the actual values end.
How can the forecasts be tamed?
dput of the input
> dput(baseTs)
structure(c(5.41951956469523, 5.49312499014084, 5.56299025716832,
5.64442852110163, 5.71385023974044, 5.77578632033402, 5.82985917237953,
5.86346591034374, 5.89626165157029, 5.92013286862512, 5.94200331713403,
5.93996840759539, 5.93917517855891, 5.90355191030718, 5.87180377346416,
5.83190030607801, 5.79624428055153, 5.75377043604686, 5.71445345904649,
5.70025269940165, 5.69789272204017, 5.73728731204876, 5.77015169357394,
5.78936321107329, 5.80113284575595, 5.79449448552444, 5.78193215198878,
5.74003482344406, 5.71694163930612, 5.66689345413153, 5.614357635737,
5.58578389962286, 5.55824727570498, 5.58495146060423, 5.61344117957187,
5.63637441850401, 5.65948408172102, 5.65558124383951, 5.64909390802285,
5.6664546352889, 5.68205689033408, 5.69991437586231, 5.72273650369514,
5.72006065065194, 5.71556512542993, 5.6717608006789, 5.64610326418084,
5.57193975508467, 5.49406607804055, 5.40126523530993, 5.31513540386482,
5.238437956722, 5.15362077920702, 5.11960611878249, 5.08498887979172,
5.08408134201562, 5.07361213981111, 5.04830559379816, 5.01401413448689,
5.0418662607737, 5.06947584464062, 5.08771495309317, 5.10587165060358,
5.1438369937098, 5.1815251206981, 5.2318657906363, 5.29385492077065,
5.29652029253008, 5.29998067741868, 5.28242409629194, 5.2722770646788,
5.24927444462166, 5.22226735874711, 5.16555064465208, 5.10956459841778,
5.09439240612378, 5.07617974794969, 5.04418337811006, 5.0075619037348,
4.99108423417745, 4.9874504485194, 4.99135285004736, 4.99217791657733,
4.94874445528885, 4.90320874819525, 4.84508278068469, 4.79086127023963,
4.75236840849279, 4.71431573721527, 4.71936529020481, 4.72422850167074,
4.72203091743033, 4.71732868614755, 4.71175323610448, 4.70566162766782,
4.71165837247331, 4.71767529028615, 4.75129316683193, 4.7863855803437,
4.85248191548789, 4.91865394024373, 4.9590849617955, 4.99960686851895,
5.02020678181827, 5.04201201976595, 5.02025906892952, 4.99735920720967,
4.92520279823639, 4.84822505567723, 4.81118504683572, 4.77330440072099,
4.72636395544651, 4.6861111959621, 4.64912520396312, 4.61348981514599,
4.58517820348434, 4.56378688913207, 4.549011597464, 4.52900600122321,
4.56028365470815, 4.60248987909752, 4.65628990381626, 4.70496326660038,
4.73779351647955, 4.76616725791407, 4.79569018347378, 4.83185281078024,
4.85177852259102, 4.87488251014986, 4.89468916229158, 4.9077984323135,
4.92375782591088, 4.96363767543938, 5.05416277704822, 5.1426680212522,
5.232495043331, 5.32153608753653, 5.41780853915163, 5.51131526881126,
5.62791210324026), .Tsp = c(2015.05769230769, 2017.73076923077,
52), class = "ts")
The code used
fc <- try(auto.arima(baseTs,ic='aic',approximation = F))
baseFc <- forecast(fc,h = weeks_forecasted)
baseVolume_forecast_new <- baseFc$mean
What could be the reason behind the forecasts exploding?
r time-series arima
asked Nov 23 '18 at 6:52
darkagedarkage
4751618
add a comment |
0
I am using ARIMA (auto.arima) to forecast for 52 weeks. The time series model fits the data well (see plot below, red line is the fitted value). The input data has a decreasing trend.
The forecasts (highlighted area) however seems to just taking off after the actual values end.
How can the forecasts be tamed?
dput of the input
> dput(baseTs)
structure(c(5.41951956469523, 5.49312499014084, 5.56299025716832,
5.64442852110163, 5.71385023974044, 5.77578632033402, 5.82985917237953,
5.86346591034374, 5.89626165157029, 5.92013286862512, 5.94200331713403,
5.93996840759539, 5.93917517855891, 5.90355191030718, 5.87180377346416,
5.83190030607801, 5.79624428055153, 5.75377043604686, 5.71445345904649,
5.70025269940165, 5.69789272204017, 5.73728731204876, 5.77015169357394,
5.78936321107329, 5.80113284575595, 5.79449448552444, 5.78193215198878,
5.74003482344406, 5.71694163930612, 5.66689345413153, 5.614357635737,
5.58578389962286, 5.55824727570498, 5.58495146060423, 5.61344117957187,
5.63637441850401, 5.65948408172102, 5.65558124383951, 5.64909390802285,
5.6664546352889, 5.68205689033408, 5.69991437586231, 5.72273650369514,
5.72006065065194, 5.71556512542993, 5.6717608006789, 5.64610326418084,
5.57193975508467, 5.49406607804055, 5.40126523530993, 5.31513540386482,
5.238437956722, 5.15362077920702, 5.11960611878249, 5.08498887979172,
5.08408134201562, 5.07361213981111, 5.04830559379816, 5.01401413448689,
5.0418662607737, 5.06947584464062, 5.08771495309317, 5.10587165060358,
5.1438369937098, 5.1815251206981, 5.2318657906363, 5.29385492077065,
5.29652029253008, 5.29998067741868, 5.28242409629194, 5.2722770646788,
5.24927444462166, 5.22226735874711, 5.16555064465208, 5.10956459841778,
5.09439240612378, 5.07617974794969, 5.04418337811006, 5.0075619037348,
4.99108423417745, 4.9874504485194, 4.99135285004736, 4.99217791657733,
4.94874445528885, 4.90320874819525, 4.84508278068469, 4.79086127023963,
4.75236840849279, 4.71431573721527, 4.71936529020481, 4.72422850167074,
4.72203091743033, 4.71732868614755, 4.71175323610448, 4.70566162766782,
4.71165837247331, 4.71767529028615, 4.75129316683193, 4.7863855803437,
4.85248191548789, 4.91865394024373, 4.9590849617955, 4.99960686851895,
5.02020678181827, 5.04201201976595, 5.02025906892952, 4.99735920720967,
4.92520279823639, 4.84822505567723, 4.81118504683572, 4.77330440072099,
4.72636395544651, 4.6861111959621, 4.64912520396312, 4.61348981514599,
4.58517820348434, 4.56378688913207, 4.549011597464, 4.52900600122321,
4.56028365470815, 4.60248987909752, 4.65628990381626, 4.70496326660038,
4.73779351647955, 4.76616725791407, 4.79569018347378, 4.83185281078024,
4.85177852259102, 4.87488251014986, 4.89468916229158, 4.9077984323135,
4.92375782591088, 4.96363767543938, 5.05416277704822, 5.1426680212522,
5.232495043331, 5.32153608753653, 5.41780853915163, 5.51131526881126,
5.62791210324026), .Tsp = c(2015.05769230769, 2017.73076923077,
52), class = "ts")
The code used
fc <- try(auto.arima(baseTs,ic='aic',approximation = F))
baseFc <- forecast(fc,h = weeks_forecasted)
baseVolume_forecast_new <- baseFc$mean
What could be the reason behind the forecasts exploding?
r time-series arima
asked Nov 23 '18 at 6:52
darkagedarkage
4751618
1
Based upon the output, it seems that the picked ARIMA model has a short memory (both on the AR and MA part). That could mean that e.g. only the last 5 data points are taken into account when the forecast is made. When that's the case, that part is indeed showing an upward trend. Try experimenting with your own ARIMA model instead of using an autofit
– CIAndrews
Nov 23 '18 at 7:09
@CIAndrews i tried doing a grid search myself setting AIC as the selection criterion. auto.aima gave a model ARIMA(1,1,2) while the grid search returned ARIMA(3,1,4) as the optimal model. Though the forecasts did decrease but the increasing trend in the forecasts remain.
– darkage
Nov 23 '18 at 7:45
Might be worth adding a seasonal element
– Jonny Phelps
Nov 23 '18 at 11:34
Seeing that the upward trend starts in the last two months of the actual data, also an ARIMA(3,1,4) wouldn't remember the downward trend before. You can try adding a linear trend fitted over the complete actual data if you want to include the downward trend in any case
– CIAndrews
Nov 25 '18 at 11:57
add a comment |
0
0
0
I am using ARIMA (auto.arima) to forecast for 52 weeks. The time series model fits the data well (see plot below, red line is the fitted value). The input data has a decreasing trend.
The forecasts (highlighted area) however seems to just taking off after the actual values end.
How can the forecasts be tamed?
dput of the input
> dput(baseTs)
structure(c(5.41951956469523, 5.49312499014084, 5.56299025716832,
5.64442852110163, 5.71385023974044, 5.77578632033402, 5.82985917237953,
5.86346591034374, 5.89626165157029, 5.92013286862512, 5.94200331713403,
5.93996840759539, 5.93917517855891, 5.90355191030718, 5.87180377346416,
5.83190030607801, 5.79624428055153, 5.75377043604686, 5.71445345904649,
5.70025269940165, 5.69789272204017, 5.73728731204876, 5.77015169357394,
5.78936321107329, 5.80113284575595, 5.79449448552444, 5.78193215198878,
5.74003482344406, 5.71694163930612, 5.66689345413153, 5.614357635737,
5.58578389962286, 5.55824727570498, 5.58495146060423, 5.61344117957187,
5.63637441850401, 5.65948408172102, 5.65558124383951, 5.64909390802285,
5.6664546352889, 5.68205689033408, 5.69991437586231, 5.72273650369514,
5.72006065065194, 5.71556512542993, 5.6717608006789, 5.64610326418084,
5.57193975508467, 5.49406607804055, 5.40126523530993, 5.31513540386482,
5.238437956722, 5.15362077920702, 5.11960611878249, 5.08498887979172,
5.08408134201562, 5.07361213981111, 5.04830559379816, 5.01401413448689,
5.0418662607737, 5.06947584464062, 5.08771495309317, 5.10587165060358,
5.1438369937098, 5.1815251206981, 5.2318657906363, 5.29385492077065,
5.29652029253008, 5.29998067741868, 5.28242409629194, 5.2722770646788,
5.24927444462166, 5.22226735874711, 5.16555064465208, 5.10956459841778,
5.09439240612378, 5.07617974794969, 5.04418337811006, 5.0075619037348,
4.99108423417745, 4.9874504485194, 4.99135285004736, 4.99217791657733,
4.94874445528885, 4.90320874819525, 4.84508278068469, 4.79086127023963,
4.75236840849279, 4.71431573721527, 4.71936529020481, 4.72422850167074,
4.72203091743033, 4.71732868614755, 4.71175323610448, 4.70566162766782,
4.71165837247331, 4.71767529028615, 4.75129316683193, 4.7863855803437,
4.85248191548789, 4.91865394024373, 4.9590849617955, 4.99960686851895,
5.02020678181827, 5.04201201976595, 5.02025906892952, 4.99735920720967,
4.92520279823639, 4.84822505567723, 4.81118504683572, 4.77330440072099,
4.72636395544651, 4.6861111959621, 4.64912520396312, 4.61348981514599,
4.58517820348434, 4.56378688913207, 4.549011597464, 4.52900600122321,
4.56028365470815, 4.60248987909752, 4.65628990381626, 4.70496326660038,
4.73779351647955, 4.76616725791407, 4.79569018347378, 4.83185281078024,
4.85177852259102, 4.87488251014986, 4.89468916229158, 4.9077984323135,
4.92375782591088, 4.96363767543938, 5.05416277704822, 5.1426680212522,
5.232495043331, 5.32153608753653, 5.41780853915163, 5.51131526881126,
5.62791210324026), .Tsp = c(2015.05769230769, 2017.73076923077,
52), class = "ts")
The code used
fc <- try(auto.arima(baseTs,ic='aic',approximation = F))
baseFc <- forecast(fc,h = weeks_forecasted)
baseVolume_forecast_new <- baseFc$mean
What could be the reason behind the forecasts exploding?
r time-series arima
asked Nov 23 '18 at 6:52
darkagedarkage
4751618
I am using ARIMA (auto.arima) to forecast for 52 weeks. The time series model fits the data well (see plot below, red line is the fitted value). The input data has a decreasing trend.
The forecasts (highlighted area) however seems to just taking off after the actual values end.
How can the forecasts be tamed?
dput of the input
> dput(baseTs)
structure(c(5.41951956469523, 5.49312499014084, 5.56299025716832,
5.64442852110163, 5.71385023974044, 5.77578632033402, 5.82985917237953,
5.86346591034374, 5.89626165157029, 5.92013286862512, 5.94200331713403,
5.93996840759539, 5.93917517855891, 5.90355191030718, 5.87180377346416,
5.83190030607801, 5.79624428055153, 5.75377043604686, 5.71445345904649,
5.70025269940165, 5.69789272204017, 5.73728731204876, 5.77015169357394,
5.78936321107329, 5.80113284575595, 5.79449448552444, 5.78193215198878,
5.74003482344406, 5.71694163930612, 5.66689345413153, 5.614357635737,
5.58578389962286, 5.55824727570498, 5.58495146060423, 5.61344117957187,
5.63637441850401, 5.65948408172102, 5.65558124383951, 5.64909390802285,
5.6664546352889, 5.68205689033408, 5.69991437586231, 5.72273650369514,
5.72006065065194, 5.71556512542993, 5.6717608006789, 5.64610326418084,
5.57193975508467, 5.49406607804055, 5.40126523530993, 5.31513540386482,
5.238437956722, 5.15362077920702, 5.11960611878249, 5.08498887979172,
5.08408134201562, 5.07361213981111, 5.04830559379816, 5.01401413448689,
5.0418662607737, 5.06947584464062, 5.08771495309317, 5.10587165060358,
5.1438369937098, 5.1815251206981, 5.2318657906363, 5.29385492077065,
5.29652029253008, 5.29998067741868, 5.28242409629194, 5.2722770646788,
5.24927444462166, 5.22226735874711, 5.16555064465208, 5.10956459841778,
5.09439240612378, 5.07617974794969, 5.04418337811006, 5.0075619037348,
4.99108423417745, 4.9874504485194, 4.99135285004736, 4.99217791657733,
4.94874445528885, 4.90320874819525, 4.84508278068469, 4.79086127023963,
4.75236840849279, 4.71431573721527, 4.71936529020481, 4.72422850167074,
4.72203091743033, 4.71732868614755, 4.71175323610448, 4.70566162766782,
4.71165837247331, 4.71767529028615, 4.75129316683193, 4.7863855803437,
4.85248191548789, 4.91865394024373, 4.9590849617955, 4.99960686851895,
5.02020678181827, 5.04201201976595, 5.02025906892952, 4.99735920720967,
4.92520279823639, 4.84822505567723, 4.81118504683572, 4.77330440072099,
4.72636395544651, 4.6861111959621, 4.64912520396312, 4.61348981514599,
4.58517820348434, 4.56378688913207, 4.549011597464, 4.52900600122321,
4.56028365470815, 4.60248987909752, 4.65628990381626, 4.70496326660038,
4.73779351647955, 4.76616725791407, 4.79569018347378, 4.83185281078024,
4.85177852259102, 4.87488251014986, 4.89468916229158, 4.9077984323135,
4.92375782591088, 4.96363767543938, 5.05416277704822, 5.1426680212522,
5.232495043331, 5.32153608753653, 5.41780853915163, 5.51131526881126,
5.62791210324026), .Tsp = c(2015.05769230769, 2017.73076923077,
52), class = "ts")
The code used
fc <- try(auto.arima(baseTs,ic='aic',approximation = F))
baseFc <- forecast(fc,h = weeks_forecasted)
baseVolume_forecast_new <- baseFc$mean
What could be the reason behind the forecasts exploding?
r time-series arima
r time-series arima
asked Nov 23 '18 at 6:52
darkagedarkage
4751618
asked Nov 23 '18 at 6:52
darkagedarkage
4751618
asked Nov 23 '18 at 6:52
darkagedarkage
4751618
asked Nov 23 '18 at 6:52
darkagedarkage
4751618
asked Nov 23 '18 at 6:52
darkagedarkage
4751618
4751618
1
Based upon the output, it seems that the picked ARIMA model has a short memory (both on the AR and MA part). That could mean that e.g. only the last 5 data points are taken into account when the forecast is made. When that's the case, that part is indeed showing an upward trend. Try experimenting with your own ARIMA model instead of using an autofit
– CIAndrews
Nov 23 '18 at 7:09
@CIAndrews i tried doing a grid search myself setting AIC as the selection criterion. auto.aima gave a model ARIMA(1,1,2) while the grid search returned ARIMA(3,1,4) as the optimal model. Though the forecasts did decrease but the increasing trend in the forecasts remain.
– darkage
Nov 23 '18 at 7:45
Might be worth adding a seasonal element
– Jonny Phelps
Nov 23 '18 at 11:34
Seeing that the upward trend starts in the last two months of the actual data, also an ARIMA(3,1,4) wouldn't remember the downward trend before. You can try adding a linear trend fitted over the complete actual data if you want to include the downward trend in any case
– CIAndrews
Nov 25 '18 at 11:57
add a comment |
1
Based upon the output, it seems that the picked ARIMA model has a short memory (both on the AR and MA part). That could mean that e.g. only the last 5 data points are taken into account when the forecast is made. When that's the case, that part is indeed showing an upward trend. Try experimenting with your own ARIMA model instead of using an autofit
– CIAndrews
Nov 23 '18 at 7:09
@CIAndrews i tried doing a grid search myself setting AIC as the selection criterion. auto.aima gave a model ARIMA(1,1,2) while the grid search returned ARIMA(3,1,4) as the optimal model. Though the forecasts did decrease but the increasing trend in the forecasts remain.
– darkage
Nov 23 '18 at 7:45
Might be worth adding a seasonal element
– Jonny Phelps
Nov 23 '18 at 11:34
Seeing that the upward trend starts in the last two months of the actual data, also an ARIMA(3,1,4) wouldn't remember the downward trend before. You can try adding a linear trend fitted over the complete actual data if you want to include the downward trend in any case
– CIAndrews
Nov 25 '18 at 11:57
1
1
Based upon the output, it seems that the picked ARIMA model has a short memory (both on the AR and MA part). That could mean that e.g. only the last 5 data points are taken into account when the forecast is made. When that's the case, that part is indeed showing an upward trend. Try experimenting with your own ARIMA model instead of using an autofit
– CIAndrews
Nov 23 '18 at 7:09
Based upon the output, it seems that the picked ARIMA model has a short memory (both on the AR and MA part). That could mean that e.g. only the last 5 data points are taken into account when the forecast is made. When that's the case, that part is indeed showing an upward trend. Try experimenting with your own ARIMA model instead of using an autofit
– CIAndrews
Nov 23 '18 at 7:09
@CIAndrews i tried doing a grid search myself setting AIC as the selection criterion. auto.aima gave a model ARIMA(1,1,2) while the grid search returned ARIMA(3,1,4) as the optimal model. Though the forecasts did decrease but the increasing trend in the forecasts remain.
– darkage
Nov 23 '18 at 7:45
@CIAndrews i tried doing a grid search myself setting AIC as the selection criterion. auto.aima gave a model ARIMA(1,1,2) while the grid search returned ARIMA(3,1,4) as the optimal model. Though the forecasts did decrease but the increasing trend in the forecasts remain.
– darkage
Nov 23 '18 at 7:45
Might be worth adding a seasonal element
– Jonny Phelps
Nov 23 '18 at 11:34
Might be worth adding a seasonal element
– Jonny Phelps
Nov 23 '18 at 11:34
Seeing that the upward trend starts in the last two months of the actual data, also an ARIMA(3,1,4) wouldn't remember the downward trend before. You can try adding a linear trend fitted over the complete actual data if you want to include the downward trend in any case
– CIAndrews
Nov 25 '18 at 11:57
Seeing that the upward trend starts in the last two months of the actual data, also an ARIMA(3,1,4) wouldn't remember the downward trend before. You can try adding a linear trend fitted over the complete actual data if you want to include the downward trend in any case
– CIAndrews
Nov 25 '18 at 11:57
add a comment |
0
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1
Based upon the output, it seems that the picked ARIMA model has a short memory (both on the AR and MA part). That could mean that e.g. only the last 5 data points are taken into account when the forecast is made. When that's the case, that part is indeed showing an upward trend. Try experimenting with your own ARIMA model instead of using an autofit
– CIAndrews
Nov 23 '18 at 7:09
@CIAndrews i tried doing a grid search myself setting AIC as the selection criterion. auto.aima gave a model ARIMA(1,1,2) while the grid search returned ARIMA(3,1,4) as the optimal model. Though the forecasts did decrease but the increasing trend in the forecasts remain.
– darkage
Nov 23 '18 at 7:45
Might be worth adding a seasonal element
– Jonny Phelps
Nov 23 '18 at 11:34
Seeing that the upward trend starts in the last two months of the actual data, also an ARIMA(3,1,4) wouldn't remember the downward trend before. You can try adding a linear trend fitted over the complete actual data if you want to include the downward trend in any case
– CIAndrews
Nov 25 '18 at 11:57