1056 Open source projects that are alternatives of or similar to msda

DeltaPy - Tabular Data Augmentation (by @firmai)

DANA: Dimension-Adaptive Neural Architecture (UbiComp'21)( ACM IMWUT)

Implementation of feature engineering from Feature engineering strategies for credit card fraud

Resources for working with time series and sequence data

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

List of tools & datasets for anomaly detection on time-series data.

This is a project based on the FIFA World Cup 2019 and Analyzes the Performance and Efficiency of Teams, Players, Countries and other related things using Data Analysis and Data Visualizations

Feature engineering package with sklearn like functionality

Automatic machine learning for tabular data. ⚡🔥⚡

Python implementation of Empirical Mode Decompoisition (EMD) method

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

An End-to-end Outlier Detection System

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

A Python toolkit for rule-based/unsupervised anomaly detection in time series

MotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope)

Merlion: A Machine Learning Framework for Time Series Intelligence

This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

Market Mix Modelling for an eCommerce firm to estimate the impact of various marketing levers on sales

fast implementation of singular spectrum transformation (change point detection algorithm)

A repository for code accompanying the manuscript 'An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series' (published at TNNLS)

Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".

A collection of time series prediction methods: rnn, seq2seq, cnn, wavenet, transformer, unet, n-beats, gan, kalman-filter

RNN based Time-series Anomaly detector model implemented in Pytorch.

A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

An intuitive library to extract features from time series

framework for developing machine (and deep) learning models for structured data

An open-source, low-code machine learning library in Python

An up-to-date & curated list of Awesome IMU-based Human Activity Recognition(Ubiquitous Computing) papers, methods & resources. Please note that most of the collections of researches are mainly based on IMU data.

Tidy anomaly detection

skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.

Anomaly detection for temporal data using LSTMs

Anomaly detection for streaming time series, featuring automated model selection.

Luminaire is a python package that provides ML driven solutions for monitoring time series data.

This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

Use advanced feature engineering strategies and select best features from your data set with a single line of code.

Exemplary, annotated machine learning pipeline for any tabular data problem.

Python machine learning package based on sklearn API for multivariate data processing and statistical analysis of symmetric positive definite matrices via Riemannian geometry

An Android framework that provides Mobile Sensing functionality to your apps.

R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.

High-performance time series algorithms for Ruby

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

⏰ This R package provides the tools to perform standard and robust wavelet variance analysis for time series (signal processing). Among others, aside from computing the wavelet variance and cross-covariance (classic and robust), the package provides inference tools (e.g. confidence intervals) and plotting tools allowing to perform some visual an…

Automated Time Series Forecasting

Kinect2 Sensor Device Driver for Linux

COVID-19 spread shiny dashboard with a forecasting model, countries' trajectories graphs, and cluster analysis tools

Indoor farming software using the Raspberry Pi

Code for my smart growbox experiment

Statistical Inference and Sure Independence Screening via Ball Statistics

Probably the best curated list of data science books in Python

Interactive Visualization to Build, Train and Test an Autoencoder with Tensorflow.js

A Custom component for Home-Assistant that checks if your Raspberry Pi power supply is giving enough voltage from the kernel.

WInte.r is a Java framework for end-to-end data integration. The WInte.r framework implements well-known methods for data pre-processing, schema matching, identity resolution, data fusion, and result evaluation.

Pitch shifter using WSOLA and resampling implemented by Python3

Prediction of continuous signals data and Web tracking data using dynamic Bayesian neural network. Compared with other network architectures aswell.

PyTorch implementation of Soft-DTW: a Differentiable Loss Function for Time-Series in CUDA

An integrated framework in R for textual sentiment time series aggregation and prediction

👁️ An authorial set of fundamental Python recipes on Computer Vision and Digital Image Processing.

Time series market data