Can features passed into Conv1d layers be randomized? If I have input time series data shape as such, X.shape = (batch_size, 50, 5), it means that the data has 5 &quot;features&quot;, each having 50 time steps. Does passing data like this into a Conv1d layer mean that the 5 features have some kind of spatial relationship with each other, like how a pixel in an image is more closely related to the pixels nearby around it than further away? Is there a way to remove this feature relationship when dealing with time series data like this? As in, even if the order of the features were randomized, the network would still learn from the patterns seen in 1d space of each feature?
Typically in a Conv1D layer for a time series, the features can be different measurements taken or recorded at the same time period. So they can be related to each other. For example, if you are trying to predict time=4 below, the question is whether there is a relationship between your features, meas1 and meas2? If so, you want to keep the features together to give you a better result.
| time | meas1 | meas2 |  
|------|-------|-------|  
| 1    | 4     | 6     |  
| 2    | 3     | 7     |  
| 3    | 2     | 8     |  
| 4    | ?     | ?     |  

You can look at cross-correlation in your dataset to answer this. Since this is time series data, you can use Pearson's correlation for this.