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I am a forecasting professional and have recently started using R.

I'm currently trying to forecast this using this code:

library(forecast)
library(tseries)
p=scan()
#scans 54 variables
p.ts=ts(p, frequency=12, start=c(2011, 01))
p.ts

       Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec
2011 102.0 102.2 102.8 103.2 103.3 103.5 103.6 104.0 104.2 103.9 104.2 104.1
2012 104.5 104.8 104.9 105.3 105.5 105.5 105.4 105.1 105.6 105.8 106.3 106.4
2013 106.4 106.4 106.6 106.8 106.4 107.0 107.5 107.4 107.6 107.9 107.9 107.7
2014 107.8 108.1 108.2 108.9 108.7 109.0 109.1 109.4 109.1 109.9 109.8 109.9
2015 109.8 109.5 109.6 109.5 109.5 109.7 110.2 110.6

plot(p.ts)

Looks like it isn't a stationary process

plot(diff(p.ts))

looks like stationary but with high variability

Then I had a look at the ACF and PACF plots enter image description here

It looks like a MA(1) process too. But not an AR process at all.

Hence, I chose to model it as ARIMA(0,1,1) process

a=arima(p.ts, order=c(0,1,1))
summary(a)

Call:
arima(x = p.ts, order = c(0, 1, 1))

Coefficients:
         ma1
      0.0757
s.e.  0.1119

sigma^2 estimated as 0.09556:  log likelihood = -13.47,  aic = 30.95

Training set error measures:
                    ME      RMSE       MAE     MPE      MAPE      MASE
Training set 0.1450371 0.3066561 0.2472274 0.13619 0.2314932 0.9853266
                   ACF1
Training set -0.2479716

Then forecasted the numbers.

f=forecast(a)

plot(f)

The forecast is just not true.

Can you help me understand where I went wrong? And how can I correct this?

This is one of five cases that I forecast. In such a case I generally model it with HoltWinters and get a decent response (one that comes very close to the realized values too).

  • maybe plot(forecast.Arima(a)) – erasmortg Oct 07 '15 at 09:04
  • @erasmortg which gives exactly the same plot. –  Oct 07 '15 at 09:12
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    A MA(1) process only has a memory of one period. This means that it converges to the unconditional mean of the process after one time period. The AR(1) model on the other hand converges exponentially towards the unconditional mean. – Plissken Oct 07 '15 at 09:43
  • NB A sharp cut-off in the partial ACF at lag 1 would suggest an AR(1) process rather than an MA(1). But as the series is clearly non-stationary, you should plot the ACF & partial ACF of the differenced series. – Scortchi - Reinstate Monica Oct 07 '15 at 10:49

1 Answers1

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ARIMA(0,1,1) is a random walk with an MA(1) term on top. The forecast for a random walk is its last observed value, regardless of the forecast horizon. The forecast for an MA(1) process is nonzero only for horizon $h=1$. Thus you get a constant forecast (equal to the last observed value plus one value of MA(1) term) beyond $h=1$. This is what you see in the graph. Nothing wrong with it.

If you expect the upward-sloping linear time trend to continue, you could consider using a random walk model with positive drift. That would give you something more consistent with the historical trend.

Richard Hardy
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