forecast rainfall using ARIMA in R

Question

I am a new student approaching ARIMA prediction analysis in R. If the question is too simple or incorrect, please forgive and guide me.

I am currently using the ARIMA provided in R. I use the data as the rainfall time series to forecast rainfall for the next several years.

My data: https://drive.google.com/drive/u/1/folders/1MmFeWoUSfXrGNbsL4SiK28FoMAby0gwD

I used the code to draw the following diagram:

datats <- ts(mydata, start = c(2000,1), frequency = 12)
datats
adf.test (datats, alternative = "stationary")
plot(datats, xlab="Year", ylab="Rainfall(mm)", main="Rainfall in Quy Nhon since 2000 to 2017", lwd=4, col="chartreuse4")
dec <- decompose(datats, type = "additive")
plot(dec, col= "firebrick1", lwd = 3)
acf (ts (datats), main="ACF For Rainfall", col="blue", lwd = 4)

Because our data has a rainfall month with a negative value, I used the command auto.arima () with the properties as follows:

ARIMAfit <- auto.arima(y=log(datats + 1), approximation = FALSE, trace = TRUE, ic="aic", test="kpss")
ARIMAfit

Code run result: Best model: ARIMA(0,0,0)(0,1,1)[12]

%Series: (log(datats + 1)) 
%ARIMA(0,0,0)(0,1,1)[12] 
%Coefficients:
%sma1
%-0.8855
%s.e.
%0.0729
%sigma^2 estimated as 1.064:  log likelihood=-304.42
%AIC=612.84   AICc=612.9   BIC=619.48

Make predictions for the next 48 months:

fact <- forecast (ARIMAfit, h=48)
fact

Code run result:

After that, I proceeded to graph the predictive data for the data using the exp() function - to convert the predicted value into the original value.

plot(exp(fact), col = "chartreuse4", lwd=3)

But this results in an error: Error in exp(fact) : non-numeric argument to mathematical function

My question is:

1. Could you please help me to see if my prediction method is accurate and how to handle errors in R?

2. Could you please help me run the code in R to predict rainfall according to the above data?

Answer 1

You asked 2 questions ...

1)Could you please help me to see if my prediction method is accurate and how to handle errors in R.

I wouldn't think so because you didn't fully extract a sufficient equation as per Help me about using ARIMA forecasting rainfall

2) Could you please help me run the code in R to predict rainfall according to the above data

can't help you there !

your arima model ( in logspace ) is essentially self-cancelling

[(1-B** 12)]Y(T) = + [(1- .885B** 12)] [A(T)]

[(1-1.0B** 12)]Y(T) = + [(1- .885B** 12)] [A(T)]

Y(T) = +{ [(1- .885B** 12)]/[(1-1.0B** 12)] } [A(T)]

or Y(T)= Y(T-12) + .885* A(T-12)

imposing an unwarranted seasonal differencing yields a now necessary seasonal ma seasonal coefficient

The errors from your model are HUGE reflecting a model that has not adequately dealt with the observed data

It devolves to a seasonal random walk forecast.

With a low R square value

R Square = .420171

Also a simultaneous plot of the actual and model errors is dispiriting