New Operator

Describe the operator

Why is this operator necessary? What does it accomplish?

This is a frequently used operator in tensorflow/keras

Can this operator be constructed using existing onnx operators?

If so, why not add it as a function?

I don't know.

Is this operator used by any model currently? Which one?

Are you willing to contribute it?

Array.reshape "only allows for reshapings that collapse or merge dimensions" (xref dask/dask#2561). However, when you try to do one of these unsupported reshapings, the error message Shapes not compatible does not make it at all clear that what you're asking for just isn't supported by dask. Instead, it sounds as though your inputs are invalid.

A more descriptive e

We currently fit and predict upon loading autosklearn.experimental.askl2 for the first time. In environments with a non-persistent filesystem (autosklearn is installed into a new filesystem each time), this can add quite a bit of time delay as experienced in #1362

It seems more applicable to export the

Once Woodwork implements this issue, we can clean up the Woodwork initialization in add_last_time_indexes to pass in the previous dataframe's table schema to keep that typing information but also perform inference on the new last time index column.

Is your feature request related to a problem? Please describe.

Financial modeling usually deals with modeling the volatility of time series data, ARCH, GARCH, etc. are common models used for this purpose.

Describe the solution you'd like

Add support for https://github.com/bashtage/arch (as an adapter) in sktime. This will be very useful for folks in the financial industry.
This

Yes

readthedocs analytics says that we have several search results that yield little or no useful results. Let's improvethose:

• gpu (only 2 results): make sure that explanation of device parameter mentions gpu as well
• gridsearch (0 results): make sure to include the term gridsearch in the meta data of

Related: awslabs/autogluon#1479

Add a scikit-learn compatible API wrapper of TabularPredictor:

• TabularClassifier
• TabularRegressor

Required functionality (may need more than listed):

• init API
• fit API
• predict API
• works in sklearn pipelines

Hello everyone,

First of all, I want to take a moment to thank all contributors and people who supported this project in any way ;) you are awesome!

If you like the project and have any interest in contributing/maintaining it, you can contact me here or send me a msg privately:

• Email: nidhalbacc@gmail.com

PS: You need to be familiar with python and machine learning