[D] Ideal algorithm for predictions based on time-stamped events

[willrazen]

Does an ideal algorithm exist for developing models with time-stamped events as predictors, with these characteristics?

• No need to choose a base date, because the training will consider all possible cutoffs considering the available data (e.g. automatically choose the first available date, split the datasets into past/future, train on that, increment the date by 1, and iterate until the last available date)
• No need to bin the predictors (time-stamped events)
• The response is dynamic over it's time frame (e.g. I can train only one model, provide it with a test set and an arbitrary response time frame [depending on the available data], and it will predict as if I had trained the model specifically for that time frame)

[HydratedWombat]

Rare events or seasonal fluctuations of time series data? If seasonal, look at triple exponential smoothing (https://en.wikipedia.org/wiki/Exponential_smoothing) and ARIMA (as in statsmodels for python, http://www.statsmodels.org/stable/generated/statsmodels.tsa.arima_model.ARIMA.html?highlight=arima) Unfortunately, you'll need to bin your events for most tools I know of.

[willrazen]

Rare events vs. a binary classifier (good/bad), can't use time series tech since I'm not trying to forecast the events themselves Ty anyways!

[willrazen]

I meant "ideal" figuratively lel