A unified framework for machine learning with time series

🌀 𝗧𝗵𝗲 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝟳-𝗦𝘁𝗲𝗽𝘀 𝗠𝗟𝗢𝗽𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 | 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟𝗘 & 𝗠𝗟𝗢𝗽𝘀 for free by designing, building and deploying an end-to-end ML batch system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 2.5 𝘩𝘰𝘶𝘳𝘴 𝘰𝘧 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 & 𝘷𝘪𝘥𝘦𝘰 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.

Introduction to Machine Learning with Time Series at PyData Festival Amsterdam 2020

A multiverse of Prophet models for timeseries

Time Series Analysis with Python Cookbook, Second Edition - Published by Packt

Base classes for creating scikit-learn-like parametric objects, and tools for working with them.

Mono repo with several data tooling subpackages to load/save/manipulate data in sinapsis

An MCP (Model Context Protocol) layer that exposes sktime’s native registry and semantics to an LLM

Python tutorial on machine learning with time series for DSSGx 2020

sktime workshops & tutorials

A collection of notebooks for my final year project. The notebooks are used to create a virtual personal trainer to check bicep curls, squats and overhead presses.

🧱 Wrappers for 3rd party models to be used with fold (https://github.com/dream-faster/fold)

Introduction to sktime: A Unified Framework for Machine Learning with Time Series

Final Submission for Google Summer of Code, 2024 @sktime

Time series anomaly detection, time series classification & dynamic time warping, performed on a dataset of Canadian weather measurements.

This project forecasts monthly healthcare call volumes using time-series modeling (ARIMA), optimizing call center staffing and reducing wait times. Features include trend analysis, model tuning, and an interactive dashboard for real-time insights.

Learn about how we can use models to make predicitons in the future based on historical data.

A time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data