I have continuous (time series) data. This data is multivariate. Each feature can be represented as time series (they are all calculated on a daily basis). Here is an example:
Days F1 F2 F3 F4 F5 Target
Day 1 10 1 0.1 100 -10 1
Day 2 20 2 0.2 200 -20 1
Day 3 30 3 0.3 300 -30 0
Day 4 40 4 0.4 400 -40 1
Day 5 50 5 0.5 500 -50 1
Day 6 60 6 0.6 600 -60 1
Day 7 70 7 0.7 700 -70 0
Day 8 80 8 0.8 800 -80 0
F1, F2, .. F5 are my features, and Target is my binary classes. If I use a window size of 3, I can convert my features into time-series data. Then, I will have [10,20,30] for feat_1, [1,2,3] for feat_2 and so on. With the window size of 3, I have 5 features* 3 window_size, a total of 15 features if written in the same vector.
The problem with this method is putting them into the same vector might cause some problems since the feature values are different.
Example of multivariate time series (15 features in 1 network):
[10, 20, 30, 1, 2, 3, 0.1, 0.2, 0.3, 100, 200, 300, -10, -20, -30]
[20, 30, 40, 2, 3, 4, 0.2, 0.3, 0.4, 200, 300, 400, -20, -30, -40]
....
[60, 70, 80, 6, 7, 8, 0.6, 0.7, 0.8, 600, 700, 800, -60, -70, -80]
The other option is to create separate time series networks (RNNs mostly, LSTM or CNN or their combination) for each of the features with the same target and then combine their results. In this scenario, I have 5 different networks, and all of them are univariate time series binary predictions.
Example of different networks with univariate time series data (3 features in 5 networks):
[10, 20, 30]
... This is for network 1
[60, 70, 80]
[1, 2, 3]
... This is for network 2
[6, 7, 8]
...
[-10, -20, -30]
... This is for network 5
[-60, -70, -80]
The problem with this one is that I might lose the feature correlation information even though I'm putting their results into another network.
My question is, which is the best way to use when dealing with multivariate time series problems? I want to use the first method, but value differences worry me. The second method is easier, but I worry I might lose some essential information.
