marcusGH/edain_paper
Contains the implementation of the EDAIN and EDAIN-KL methods proposed in our paper. The research was also part of the thesis I wrote as part of my MSc in Statistics (Data Science) at Imperial College London
This project offers a novel way to prepare complex time series data for machine learning models, especially deep neural networks. It takes raw, irregular time series data—like financial trading logs or customer credit histories—and adaptively normalizes it. The output is preprocessed data that significantly improves the accuracy and efficiency of predictions or classifications. This is ideal for data scientists or quantitative analysts who build predictive models from real-world, noisy time series.
No commits in the last 6 months.
Use this if you are building deep learning models for time series data with irregularities like multiple modes, skewness, or outliers, and you need a more effective preprocessing method than standard normalization.
Not ideal if your time series data is already well-behaved, or if you are not using deep neural networks for your prediction or classification tasks.
Stars
16
Forks
—
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 19, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/marcusGH/edain_paper"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
recodehive/Stackoverflow-Analysis
Stack overflow is a professional community for developers. This repo analysis 3 years of...
comet-ml/kangas
🦘 Explore multimedia datasets at scale
CrowdStrike/omigo-data-analytics
Data Analytics Library for Python
afraniomelo/KydLIB
Routines for exploratory data analysis.
rojaAchary/Data-Visualization-with-Python
Data visualization is the visual presentation of data or information. The goal of data...