Time Series Analysis and R Holt Winters - r

Question

I have a seasonal (7 days interval) time series, daily data for 30 days.
What is the best approach for a reasonable forecast?
The time series contains orders made with a app, it shows a seasonality of 1 week (lower sales at the beginning of the week).
I try the holt winters approach with this code:
(m <- HoltWinters(ts,seasonal = "mult"))
plot(m)
plot(fitted(m))
but it gives me an error like: Error in decompose(ts(x[1L:wind], start = start(x), frequency = f),seasonal) :
time series has no or less than 2 periods
What do you suggest?
EDIT:
data here

Answer 1

You must first determine a ts object. Assuming your data is called df:
ts <- ts(df$install, frequency = 7)
(m <- HoltWinters(ts,seasonal = "mult"))
plot(m)
plot(fitted(m))
Then you can make prediction like (10 steps-ahead):
predict(m, n = 10)
Time Series:
Start = c(4, 5)
End = c(5, 7)
Frequency = 7
fit
[1,] 1028.8874
[2,] 1178.4244
[3,] 1372.5466
[4,] 1165.2337
[5,] 866.6185
[6,] 711.6965
[7,] 482.2550
[8,] 719.0593
[9,] 807.6147
[10,] 920.3250
The question about the best method is too difficult to answer. Usually one compares the performance of different models considering their out-of-sample accuracy and chooses the one whith the best result.

Answer 2

You can use df$data to keep the dates that correspond to each day in the ts series.
ts_series <- ts(df$install, frequency = 7)
ts_dates <- as.Date(df$data, format = "%d/%m/%Y")
In a similar way, dates for the forecasted values can be kept in another sequence
m <- HoltWinters(ts_series, seasonal = "mult")
predict_values <- predict(m, 10)
predict_dates <- seq.Date(tail(ts_dates, 1) + 1, length.out = 10, by = "day")
With the dates sequence, the daily series can be plot with dates in x axis with the right format. More control on the x axis ticks can be obtained with the axis.Date function
plot(ts_dates, ts_series, typ = "o"
, ylim = c(0, 4000)
, xlim = c(ts_dates[1], tail(predict_dates, 1))
, xlab = "Date", ylab = "install", las = 1)
lines(predict_dates, predict_values, lty = 2, col = "blue", lwd = 2)
grid()