- I have a univariate daily time series.
- My data consists of three years of data.
- There are no missing values
- The data exhibits strong seasonalities
- I can be absolutely sure that the seasonalities for the future are verisimilar to those of the past, e.g. before Christmas we sell more
- All values are positiv integer values ranging from 1 000 to 100 000
- There dataset exhibits a trend. The trend is not linear.
- There dataset is trend-stationary/ close to trend-stationarity. There are seasonalities, but there are not Unit roots.
- The data is the number of items my company sells per day, but I would like to use the same algorithm for the daily revenue and for several other KPIs.
I would like to forecast the next six months of daily data. It is important that the daily values are as accurate as possible, but the algorithm should also deliver a reasonable amount for the total amount of items sold in the upcoming six months. If possible I want to use a neural net, e.g. a recurrent neural net or a feed-forward neural net.
What I have tried so far:
Until now I have tried
auto.arima from the forecasting packages, as well as some other autoregressive models and the
nnet command from the forecast package in R. I tested the models by forecasting the last 6 months with the data and comparing it to the actuals. Among all the models I used the arima model delivered the best results.
- Could you also provide me with some code in R?
- Can I also take your model when some of my assumptions change, e.g. when there is a unit root or when I have to forecast for an entire year?