Given the following daily time series data
I have used auto.arima in R to build a model. I used freq = 5
because the data is collected only on weekdays (Monday through Friday).
tsdata <- ts(data, freq = 5)
fit <- auto.arima(tsdata, ic = 'aicc')
plot(forecast(fit, h = 80))
return,
Series: tsdata
ARIMA(3,1,2)(0,0,1)[5] with drift
Coefficients:
ar1 ar2 ar3 ma1 ma2 sma1 drift
1.6825 -0.6384 -0.0993 0.0151 -0.9778 -0.0063 -0.0232
s.e. 0.0494 0.0904 0.0487 0.0125 0.0123 0.0483 0.2080
sigma^2 estimated as 33.92: log likelihood=-1393.24
AIC=2806.1 AICc=2806.44 BIC=2838.78
I obtained the following forecast result:
Why does Arima only give me back a straight line ?
Is the ARIMA model is the correct one to use here ?
How do I retain the feature of the forecast data ?
I am very new to build time series model.
Any suggestions would be appreciated.
Best Answer
There's quite a lot to fitting an ARIMA model properly. As with most statistical models there are things you need to do to check that the modelling assumptions are correct and that you've chosen the most appropriate form. The auto.arima makes some sensible choices for you but to understand these choices you'll need to do some background reading. Here's a useful place to start http://people.duke.edu/~rnau/411arim.htm
However a short answer to your question is that there apparently isn't much by way of trend or seasonality in your time series and therefore the best forecast is a straight line. (It's often difficult to convince people that straight line is the best forecast - they want to see something complicated looking in order to be convinced).