One issue is that the number of historical data points used in the model is really very very small. The author's point out that the model initially was fit to 1

One issue is that the number of historical data points used in the model is really very very small. The author's point out that the model initially was fit to 1,152 CDC data points. However, these 1,152 data points are highly temporally correlated (the value at time t is a strong predictor of the value at time t+1). As such, they aren't "worth" very much when building a model. Therefore, the effectively independent number of points (what you generally need to build a good model) is in reality much smaller. Looking at the graphs, it appears that for each flu season there is a pretty regular spike of flu incidences. This spike could probably be summarized effectively by four parameters: - Mean - Standard Deviation - Skewness - Kurtosis Looking at it this way, the number of "independent" data points of a flu season is not 365 (one for every day the year), 52 (one for every week), or even 12 (one for every month). Instead it is only 4. Thus the total amount of data they had to build their model was 4*(# years). When you have such small data sets, you can't expect to obtain good results. TL;DR. Time series are very hard to use in predictive models. It is often impossible to generate good predictive models based off them.