Time Series Model for multiple trends 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)

Edit:

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.
I think you should look into multivariate regression. You could use these variables (type, area etc) along with other factors such as seasonality. Create dummy variables such as date of the week, week number , month etc to capture seasonality. For example rainy month may have less bike demand. These are inherent features from the data.