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Implementing PatchTST but on a different type of supervised data
Hi @oguiza , thank you so much for integrating PatchTST to tsai and for your helpful tutorial in the Google Colab. But I have a bit of different data, and I need some help in applying the patchTST model from tsai in it.
I have a time series Twitter dataset that looks like this. The first column is the time
column, and the rest are the user id
. Each row of the user ID column has a toxicity value
for that time.
The dataset looks like this
time | 378843212 | 1246821236 | 32186559 | 65458298 | 19017659 |
---|---|---|---|---|---|
2017-01-01 00:00:03 | 0.041532 | 0.038335 | 0.047807 | 0.050232 | 0.034241 |
2017-01-01 01:00:03 | 0.041532 | 0.038335 | 0.047807 | 0.050232 | 0.034241 |
2017-01-01 02:00:03 | 0.041532 | 0.038335 | 0.047807 | 0.050232 | 0.034241 |
2017-01-01 03:00:03 | 0.041532 | 0.038335 | 0.047807 | 0.050232 | 0.034241 |
2017-01-01 04:00:03 | 0.041532 | 0.038335 | 0.047807 | 0.050232 | 0.034241 |
So now, using the patchTST model, I want to forecast this time series for each user, so my model should be able to tell a toxicity value for a user at a certain time. How can I do this? It would be of great help to me if you can help me out?
Hi @aatmanvaidya, this is not yet currently supported by PatchTST. For now, you can only use it with univariate datasets, or multivariate datasets where you predict all input variables (in fact, this is duplicate of #713).
Hello @oguiza, understood, thank you so much for your reply. Apologies, I missed out that a similar issue already existed.