Yes, you can use 'datetime' as an input variable for your models, but you will need to preprocess it appropriately. Raw datetime values are not directly useful for most machine learning models. Here are few techniques to transform datetime into useful features:
- Extract Temporal Features: Break down the datetime into more granular components that can capture temporal patterns. For winter data, you might consider:
- Hour of the day
- Day of the week
- Day of the month
- Week of the year
- Whether it's a weekend or a weekday
2. Cyclical Encoding: Since time has a cyclical nature (e.g., hours in a day, days in a week), you can use cyclical encoding to represent these features:
For hours:
- hour_sin = sin(2 * π * hour / 24)
- hour_cos = cos(2 * π * hour / 24)
Similarly, you can encode day of the week, month, etc.
3. Seasonality Indicators: Since you are focusing on winter data, you might want to create a binary feature indicating if the datetime falls within typical winter months for your location (e.g., December, January, February).
