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I am working with daily data (variables include: temperature, salinity, wind, etc...) from 2002-2013 (msts), and I want to identify the ARIMA equation describing the whole data set, while also considering covariates unique to each variable, then use the ARIMA equation to predict each variables' values 7 days into the future from specific starting points in the data set. Thus, tbats is not appropriate for me.

First I need to define the "order" and "seasonal" values for my Arima equation using auto.arima. Additionally, I believe there is seasonality in the data that is weekly, monthly and yearly so I am defining multiple "seasonal periods" in auto.arima. Starting with predicting the temperature variable values, I am using xreg in the auto.arima function to include specified covariates, and want to also include the fourier/fourierf function for the dummy variables of month and year as well as a method for determining K.

Ultimately, my R code should be written to consider long-term data (2002-2013), seasonality (3), dummy variables (Month and Year), and identify the K value for predictive purposes.

Although I have found a lot of great help online, I cannot get my more complex code to work. http://robjhyndman.com/hyndsight/longseasonality/ (and other Hyndman posts for complex seasonality, forecasting, detecting seasonality, daily data, etc...) and Time Series Forecasting with Daily Data: ARIMA with regressor

I am using these packages for various parts of the code development:

library(MASS)
library(mgcv)
library(lattice)
library(epicalc)
library(caTools)
library(forecast)
library(McSpatial)

My simple R CODE below works - although only has one seasonal period, and no fourier function or dummy variables included.

Season<- msts(PaulsData$Temperature, seasonal.periods=30)
auto.arima(Season,stepwise=TRUE,approximation=TRUE,xreg=Covariates)
# Temperature: ARIMA(4,1,0)(1,0,0)[30]
fit<-Arima(June2009EventA$Temperature,order=c(4,1,0),seasonal=c(1,0,0))
plot(forecast(fit, h=7)) # fit is not very dynamic
dev.off()
View(forecast(fit))

The complex code below has the equation components I think I need, but it does not work... The K part is where things keep getting hung up... "Error in fourier(y, K = i) : unused argument (K = i)". This happens anytime I use fourier. 

y<- msts(PaulsData$Temperature, seasonal.periods=c(7,30,365.25)) #Daily Temperature (w/NAs) 2002-2012
bestfit<-list(aicc=Inf) # Select K value
for(i in 1:25)
{
fit<- auto.arima(y, xreg=fourier(y, K=i), seasonal=FALSE) # need the K-value, error in fourier function - K unused arugument
 if(fit$aicc<bestfit$aicc)
   bestfit<-fit
 else break;
}
dummyMonth<- fourier(msts(PaulsData$Temperature,seasonal.periods=cbind(7,30,365.25), ts.frequency=30),K=bestfit)   # need the K-value, error in fourier function - K unused argument
ZdummyMonth<- fourierf(msts(PaulsData$Temperature,seasonal.periods=cbind(7,30,365.25), ts.frequency=30, h=7),K=bestfit)
dummyYear<- fourier(msts(PaulsData$Temperature,seasonal.periods=cbind(7,30,365.25), ts.frequency=365.25),K=bestfit)
ZdummyYear<- fourierf(msts(PaulsData$Temperature,seasonal.periods=cbind(7,30,365.25), ts.frequency=365.25, h=7),K=bestfit)
fit<- auto.arima(y,xreg=cbind(dummyMonth,dummyYear,Covariates),seasonal=FALSE)
plot(forecast(fit, h=7))

Even if I use the code below to exclude the selection of the K value, it does not work?!

y<- msts(PaulsData$Temperature, seasonal.periods=c(7,30,365.25)) #Daily Temperature (w/NAs) 2002-2012
dummyMonth<- fourier(msts(PaulsData$Temperature,seasonal.periods=cbind(7,30,365.25), ts.frequency=30),K=5)   # need the K-value, error in fourier function - K unused argument
ZdummyMonth<- fourierf(msts(PaulsData$Temperature,seasonal.periods=cbind(7,30,365.25), ts.frequency=30, h=7),K=5)
dummyYear<- fourier(msts(PaulsData$Temperature,seasonal.periods=cbind(7,30,365.25), ts.frequency=365.25),K=5)
ZdummyYear<- fourierf(msts(PaulsData$Temperature,seasonal.periods=cbind(7,30,365.25), ts.frequency=365.25, h=7),K=5)
fit<- auto.arima(y,xreg=cbind(dummyMonth,dummyYear,Covariates),seasonal=FALSE)
plot(forecast(fit, h=7))

Where: Temperature = a column of values in deg C with x-rows
Month = matrix of zeros and ones with x-rows
Year = matrix of zeros and ones with x-rows
Covariates = columns of values in appropriate units (i.e. Salinity (ppt), Wind (m/s)) with x-rows

Thank you in advance for your help!

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    $\begingroup$ Load forecast library and type ?fourier to see the help page for this function. The last example there is for multivariate time series. It looks like you need to supply a vector K, not a scalar K. Maybe that is why you are getting an error. $\endgroup$
    – Richard Hardy
    Commented Jan 30, 2015 at 9:23

1 Answer 1

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Try:

library(forecast)
ts_ <- ts(PaulsData$Temperature, frequency = 1)
msts_ <- msts(ts_, c(7,30,365))
fit <- auto.arima(ts_, seasonal=F, xreg=fourier(msts_, K=c(3,5,10)))  # i,j,k

If this works, you may loop through (i,j,k) to find the "best" model. Make it sure (i,j,k) is no more than half of (7,30,365) correspondingly.

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    $\begingroup$ what it means > k=c(3,5,10) i am having daily temperature data for 25 years how can use this ? because when ever i am forecasting it is showing me error let say arima1 is fitted so when i used to forecast arima1.forecast=forecast.Arima(arima1,h=306) It shows me below error Error in forecast.Arima(fit, h = 306) : No regressors provided $\endgroup$
    – Hiren
    Commented Mar 15, 2017 at 13:38

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