Greetings! It is a pleasure to be here. I became acquainted with Nixtla through a recent LinkedIn post by Max, and I must say that I am thoroughly impressed. The creation of a dedicated ecosystem for time series modelling is an invaluable resource, and I have already begun to utilize it. After thoroughly exploring the Nixtla GitHub repositories, I discovered state-of-the-art solutions for time series forecasting. I am curious, does your company have plans to extend the ecosystem to include other tasks such as time series classification, unsupervised time series clustering, and the like? These fields are increasingly popular, yet there are currently few frameworks that can handle time series data for these tasks, and the most widely used method of employing RNN/LSTMs is computationally expensive. If your team is planning to delve into these areas, I am eager to contribute to both the research and implementation aspects in an open-source capacity. Currently, the only existing solution for these tasks is the sktime package in Python. While similar to sklearn, it is less intuitive to use and may not yield the most accurate results. cc: @Max (Nixtla) @fede (nixtla) (they/them)

Hey @Poojan Vachharajani! Thank you for your kind words and using our libraries. Yes! We are planning to extend the ecosystem to include classification, clustering and other interesting use cases. Please help us opening an issue in our repo, we could start planning how to colaborate there :)