I'd like to build a model based on time series. I have a dataset with records every 30 minutes for three months.
What is the difference between modeling these data with the following kinds of models?
- Extracting hour/week-day/month and use them as features in machine learning algorithms
- Using ARMA models
My data contains weather information. One of scenarios I am working on is predicting "the use of bikes", it's related to information like weather/temperature/wind/time (day/hour, I think that month doesn't make sense) ... In such scenarios, should I use a time series ARMA models or just extract hour/week-day/month and use them as features to apply algorithms like tree/random-forest.
Can any one explain the difference, or point to paper/book to check?
Note: I am self-learner, didn't attend any data science class. Apologies if this is obvious.