We have three years of data for online visits at a daily level. We want to forecast the daily visits for the next 90 days. What would be the best method to capture weekday seasonality , holiday seasons, and also the drift.
Can this be successfully done in R? We are currently using R. We have considered ARIMA but it does not capture seasonality.
While converting the data to a time series in R, what should be the "frequency"?
Should we use ARIMA with regressors?
astsaR package) or P(eriodic)ARMA (as covered in the
partsmR package) class of models. Complex (multiresolution) seasonality can be captured using, for example, Hyndman's
tsbats()function. Forecasting daily time series is not something that there is a lot of literature on, but I will try to post a short literature survey if time permits. $\endgroup$
forecastlibrary it has a feature to combine Loess decomposition (
stl()) with its
forecast()function. Try the follwing:
install.packages("forecast");library(forecast);fit <- stl(USAccDeaths,s.window="periodic");plot(forecast(fit))see also
arimacaptures seasonality, and
auto.arima. This question displays no research effort. $\endgroup$