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

Python tutorial on machine learning with time series for DSSGx 2020

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

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

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

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

The second attempt at tne multinomial series. Not for commercial use.

Time Series Classification experiments on open-source datasets using Automated Machine Learning (AutoML) frameworks

🚀 Final Submission for Google Summer of Code, 2024 at @sktime

An easy and effective application to get insights and predict the future.

Predicting price trends in crypto (BTC_USDT) using lstm, rnn, sklearn, sktime, tff, etc.

Hyperparametrization test with a genetic algorithm