(1/2) Modeling Short Time Series with Prior Knowledge
When modeling time series data, you may find yourself with insufficient data. Insufficient data in time series would typically be defined as less than one seasonal cycle. This would challenge us to understand whether some events are driven by seasonality or other reasons, such as one-time events, outliers, etc.
(2/2) If you have some prior knowledge about the series (e.g., learning from similar products or goes, etc.), you should consider using the Bayesian approach.
The article below by Tim Radtke provides an example of how to incorporate prior assumptions into a time series forecasting model when having insufficient data.
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