Hey. I am trying to model some multimodal time-series data. The data has 3 “components” - a time-series component (`shape = [timesteps, channels, batchsize]`

), and 2 separate flattened components (`shape = [numfeatures, batchsize]`

). This is going to be a regression task.

For my particular data the shapes are `[50, 3, 64]`

, `[4, 64]`

and `[1, 64]`

. The output is of shape `[12, 64]`

.

I’m a little new to Flux (and Julia in general) and was wondering how one would model this?

My dataloader returns a tuple of xs and ys where xs is also a tuple of (timeseries, flattenedmatrix, flattenedmatrix).

```
for ((tfw, tfs, tfp), tftgt) in traindl
@show tfw |> size tfs |> size tfp |> size tftgt |> size
break
end
tfw |> size = (50, 3, 64)
tfs |> size = (4, 64)
tfp |> size = (1, 64)
tftgt |> size = (12, 64)
```

I am a little new to time-series modeling as well so if there’s any resource to help me model time-series well, that would be cool as well. And also any way to add augmentations to time-series is also welcomed as currently I am not adding any data augmentation.

I also cannot for the life of me figure out how to use the `LSTM`

in Flux. Any idea if `Self-Attention`

would be nice thing to try as well?

Thank you!