Reputation: 87
Whenever I try to fit a model using auto.arima I get an error
auto.arima can only handle univariate time series
But I have converted the data into time series. Can anyone help?
library(forecast)
sales = data.frame(
Year = c(2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018),
Qtr1 = c(2.32,4.36,8.74,16.24,37.04,47.79,51.03,74.47,74.78,78.29,77.32),
Qtr2 = c(1.7,3.79,8.75,18.65,35.06,37.43,43.72,61.17,51.19,50.76,52.22),
Qtr3 = c(0.72,5.21,8.40,20.34,26.03,31.24,35.20,47.53,40.40,41.03,41.30),
Qtr4 = c(6.89,7.37,14.1,17.07,26.91,33.8,39.27,48.05,45.51,46.68,46.89))
sales
plot(sales)
salests = ts(sales)
tsdisplay(salests)
arima_fit = auto.arima(salests,stepwise = FALSE, approximation = FALSE) ##ERROR
a_f = forecast(arima_fit, h =8)
plot(a_f)
Upvotes: 0
Views: 550
Reputation: 8267
Your salests
is a matrix containing five time series, one for each column. The first time series is called Year
, then Qtr1
through Qtr4
. This is probably not what you wanted.
Take your sales
data, convert it into a matrix, drop the first column (which contains the years), transpose it, convert it into a vector
and convert this into a time series:
salests <- ts(as.vector(t(as.matrix(sales)[,-1])),frequency=4,start=c(2008,1))
Upvotes: 1