I have a monthly time series of online visits for last 3 years starting from Jan 2016 to Dec 2018 and need to forecast for 2019. The data clearly has an upward trend although no seasonal lags significant. I assumed i will get a forecast where there is an upward trend as there is trend historically along with some variations but what i got is flat forecast.
Various answers in earlier post suggests flat forecast is acceptable either for white noise or random walk series but that is not the case here.
acf and pacf plot shows correlation exist although no seasonal lags significant
model <- auto.arima(data_ts) #,lambda = best.lambda summary(model)
On forecasting for next 12 months of 2019 these is what i got
forecasts <- forecast(model,12) plot(forecasts)
If i go with the seasonal plot of both historical data along with 2019 forecast these is how it looks
The series seems to be forecastable and therefore shouldn't my 2019 forecast bit higher if i go by the past pattern? I did transformation also and values are nearby giving almost flat line. Is there anything that i need to take care off? Any suggestion is highly appreciated!
Here is my data:
library(forecast) a <- c(1056839, 1326049, 1042199, 1738067, 1647886, 1400829, 1268237, 2146656, 1036955, 1118508, 1287044, 1501017, 3143967, 2092133, 2576878, 3184591, 2803422, 3064013, 4235912, 1573469, 1962265, 2044005, 3820864, 1995260, 5441314, 2406231, 4009222, 4402667, 4717253, 4564327, 6128167, 7649497, 3919549, 3408183, 4677126, 3202102) data_ts <- ts(a,start = c(2016,01),frequency = 12) autoplot(data_ts)