CODEWITHSUNDEEP
rtime-series
Mike C
Mike C

Reputation: 2049

R ARIMA model giving odd results

I'm trying to use an ARIMA model in R to forecast data. A slice of my time series looks like this:

enter image description here

This is just a slice of time for you get a sense of it. I have daily data from 2010 to 2015.

I want to forecast this into the future. I'm using the forecast library, and my code looks like this:

dt = msts(data$val, seasonal.periods=c(7, 30))
fit = auto.arima(dt)
plot(forecast(fit, 300))

This results in:

enter image description here

This model isn't good or interesting. My seasonal.periods were defined by me because I expect to see weekly and monthly seasonality, but the result looks the same with no seasonal periods defined.

Am I missing something? Very quickly the forecast predictions change very, very little from point to point.

Edit:

To further show what I'm talking about, here's a concrete example. Let's say I have the following fake dataset:

x = 1:500
y = 0.5*c(NA, head(x, -1)) - 0.4*c(NA, NA, head(x, -2)) + rnorm(500, 0, 5)

This is an AR(2) model with coefficients 0.5 and 0.4. Plotting this time series yields:

enter image description here

So I create an ARIMA model of this and plot the forecast results:

plot(forecast(auto.arima(y), 300))

And the results are:

enter image description here

Why can't the ARIMA function learn this obvious model? I don't get any better results if I use the arima function and force it to try an AR(2) model.

Upvotes: 1

Views: 584

Answers (1)

Rob Hyndman
Rob Hyndman

Reputation: 31820

auto.arima does not handle multiple seasonal periods. Use tbats for that.

dt = msts(data$val, seasonal.periods=c(7, 30))
fit = tbats(dt)
plot(forecast(fit, 300))

auto.arima will just use the largest seasonal period and try to do the best it can with that.

Upvotes: 1

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