I am a newbie in the ML field. So please, neglect or better correct, if I am wrong somewhere. Currently working on a model training for time series data. My problem is a little more specific to bike-sharing. I have a count of bike-sharing for each area and for each bike type(gear, without gear...) for each day.
For example, Data:
Date BikeType Area BikeCount 1/1/19 Gear 1 10 1/1/19 WithoutGear 1 15 1/1/19 Gear 2 8 1/1/19 WithoutGear 2 12 2/1/19 Gear 1 11 2/1/19 WithoutGear 1 17 2/1/19 Gear 2 9 2/1/19 WithoutGear 2 16
So, I will have a trend for each type of bike for each area. How to use time series for this data. I have to predict the bike required for each Type and each Area. For example, for the given data I have to predict the count of Geared bike required and WithoutGeared bike required for both 1 and 2 areas on 3/1/19. (Considering two dates' data is enough for prediction, I have 2 years data for each specific area and type, they have a good trend)
And the second question is... Currently, I have to only two dimension bike types and area, they may increase later(like their color and their condition) how to handle this. Any contribution will be helpful.
I get a hint from the question: https://stackoverflow.com/questions/55545501/how-to-perform-time-series-analysis-that-contains-multiple-groups-in-python-usin
But, is this the best any only way...
Thank you (I need suggestion for the question title also)
I get a similar problem in the following references:
Multi-dimentional and multivariate Time-Series forecast (RNN/LSTM) Keras
Multivariate and multi-series LSTM
Now, I have two more doubts:
Is the LSTM is the only way?
Are my data columns (data types and area) dimensions or features.