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Is there a simple real-valued time-series dataset on which a vanilla RNN model can be trained. With "very simple" I mean only two to four real-valued inputs per time step and a single real-valued output per time-step.

Background: I am doing research in the intersection of machine learning and formal methods. To test a new technique for formally verifying RNNs, we need to start with a quite simple setup.

Thanks!

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You can take a look at some of these datasets in UCI:

https://archive.ics.uci.edu/ml/datasets.php?format=&task=&att=&area=&numAtt=10to100&numIns=&type=ts&sort=attUp&view=table

These are quite straight forward, although they might contain more columns that 4 or 5. But they are quite simple.

Or you can take a look at this:

https://www.kaggle.com/uciml/electric-power-consumption-data-set

This is also based on UCI data.

Overall you can take a look at datasets available on Kaggle, you can probably find something there.

https://www.kaggle.com/datasets

Hope this helps.

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These are my favourite time series datasets.

These two are quoted a lot in the literature on the imputation of missing data in time series:

If you are not interested in fighting with missing values (yet), I suggets you to take a look at the first two. There's plenty of work to do on all though, either univariate or multivariate analyses. You can also use Kaggle kernels and the literature as a benchmark to evaluate your own results. Good work, have fun.

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