1. The problem statement, all variables and given/known data We've got a random variable that appears to have no trend/seasonality, is stationary, and we want to forecast it. The variable is number of warranty claims received each day, 53 days, so we've got 53 values, and we want to forecast the values of the upcoming 5 days. 2. The attempt at a solution I'm trying to model the data using ARIMA model. Judging from the autocorrelation plots, the data is stationary, so no differencing should be done. Judging from the ACF and PACF plots, our best bet would be AR(1), MA(1) or ARIMA(1,0,1). All yield similar results: Is there no better way to forecast this variable? ARIMA does not seem like a good forecasting option in this case. The data has no apparent trends/seasonalities and is stationary. What method would be best to forecast such data?