sktime

Mentors: Student: Pranav Prajapati

The idea behind this project is to integrate data backends like Polars and deep learning foundation models from libraries like Hugging-Face and building interfaces for them in sktime. Adding support to polars Series and DataFrame as mtypes which will represent sktime compatible scitypes will be a part of scaling backends for sktime datatypes. This includes writing/enhancing adapter for Polars, and implementing polars mtypes as an abstract data type, a scitype(Panel, Series). Adding Polars support to interfaced models that already support polars so users can directly pass Polars DataFrame for training and prediction in sktime.

Mentors: Student: Abhay Lejith

sktime in its current state does not support categorical variables in input data and this has been an often requested feature from its users. This project aims to add categorical support (for exogeneous input) to the forecasting and transformation modules by reworking the base framework layer and datatypes module. Another objective is to ensure categorical data works when used in sktime pipelines and relevant compositors.

Mentors: Student: fnhirwa

With Sktime’s goal of unifying machine learning with time series, I intend to contribute to that mission with the project titled “Scaling backends, foundation models, PyTorch, darts and pytorch-forecasting with sktime.” Where I’ll mainly be working on the integration with deep learning backends PyTorch through third-party library pytorch-forecasting and time series anomaly detection library darts. With the growth of the usage of deep learning networks in solving time series-related problems, it is needed to integrate sktime with the powerful deep learning libraries, and with this, I am interested in contributing to the Sktime project in this particular subject.

Mentors: Student: Armaghan

Sktime provides a user-friendly interface for a range of forecasting algorithms and time-series functionalities. To enhance its capabilities further, we can integrate deep learning models from pytorch and huggingface. This integration will significantly extend Sktime’s functionality by incorporating advanced deep learning architectures, foundational models, and global forecasting capabilities.

Mentors: Student: XinyuWu

This project enables global forecasting in sktime. 1. design and implement global forecasting API in sktime. 2. add pytorch-forecasting models with global forecasting API. 3. extend HFTransformersForecaster, NeuralForecast models to global mode. 4. extend forecasting benchmark and NaiveForecaster to global mode. 5. documents and notebooks for global forecasting in sktime.