When doing supervised learning classification, how can I account for seasonal data?
Here is a very simple example to consider
- Classify loans as either being defaulted on or not
- Data entries have a numerical loan amount and a date
Time is important here because there can be seasonal trends in spending that can help classify if a loan will be defaulted on or not.
Is there an algorithm or approach to classifying that can account for this seasonality in data?
I have looked online and there are recommendations for the Hidden Markov Model. However from my understanding, the HMM for time series would classify based on a direction of trend. In the context of the given problem, classifying the direction of trend is different than classify on defaulting or not.
Please let me know if there is another stackexchange site is more appropriate for this questions.