First question: How many layers?
This is architectural question and one of them most important when constructing NN. Generally the more complex the task the more layers you should use to approximate (until a certain point than there is overkill, motivation for ResNet)
If you are looking for some guidelines there are some good posts, but the research and ...
Question 1. Both. If you think in opposite to multivariate terms, than in univariate regression both input and output variables should be 1-d
Question 2. Multivariate regression where more than one independent variable (predictors) and more than one dependent variable (responses), are linearly related. So input needs to be more than 2 also.
Sorting data won't affect the training of your model, it is similar to changing the random seed.
It can affect the validation that you are doing. In case you do time series you can do sliding window or roll-out-window, that they need the data to be sorted before the splitting.
It seems that you want to do time series regression with supervised learning so ...
To answer your questions in order:
The whole point of LSTM (or any time series model) is to predict previously unseen values. If states are not reset - then there is a risk of data leakage - whereby forecasts from the training set will lead into the test set. This would mean that your model would perform quite well on the existing set of data - but would ...
If I understand you correctly, I think I have a simple way and you do not need to overthink it.
Take a Gaussian (Normal) distribution random number generator, it could even be just downloading a set from https://www.random.org/gaussian-distributions/
Multiply by the desired standard deviation, add the desired mean.
From a certain timestamp, change the ...
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.
To answer your points in order:
I can generally understand what you are trying to do. However, a few considerations you should bear in mind:
1.1. Given that you are trying to make time series predictions for all customers, then you are most likely interested in a panel data modelling solution - i.e. one that takes into account the fact that the data is ...
I'm assuming the displayed time series shows number of jobs submitted per 15 minute interval.
Divide the time series per category. If the jobs can be divided into type1, type2, type3 then make a time series for each type and predict each series individually. So type1-time series has number of type1-jobs per 15 minute interval.
Lambda is a tuning parameter („how much regularisation“, I think called alpha in sklearn) and you would choose lambda so that you optimise fit (e.g. by MSE). You can do this by running cross validation.
This page (for the GLMnet package in R) explains how to apply Lasso in a very instructive way (alpha is the elastic-net mixing parameter here, Lambda is ...