timeseries-analysis

A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile

Anomaly detection

This tool should help discover different patterns based on similarity measures in historical (financial) data

A simple and flexible code for Reservoir Computing architectures like Echo State Networks

Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock market. In this work, we present an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data using a deep-learning based approach combined with discrete signal processing on the time series data. In addition, we use a tree-based approach that visualizes events and actions to aid analysts in their understanding of large amounts of unstructured data. Using existing data, we have discovered that our approach has a good success rate in detecting illegal insider trading patterns. My research paper (IEEE Big Data 2018) on this can be found here: https://arxiv.org/pdf/1807.00939.pdf

An easy to use low-code open-source python framework for Time Series analysis, visualization, forecasting along with AutoTS

Hybrid Time Series using LSTM and Kalman Filtering

Python based Quant Finance Models, Tools and Algorithmic Decision Making

interpretable gaussian processes for stellar light curves

Performant, composable online learning

Predication of stock market price using different machine learning models

A project with tools for simple timeseries analysis

Artificial Neural Networks in Economic Forecasting (ANNEF)

Python Framework to Manage Time Series structured as one-level dictionaries.

Python framework to manage time series.

A general purpose, multi-variate anomaly detection algorithm using machine learning

Code for paper "Anomaly Detection of Wind Turbine Time Series using Variational Recurrent Autoencoders."

Project is based on the paper "Early classification on multivariate time series". Author Guoliang He, Yong Duan, Rong Peng, Xiaoyuan Jing, Tieyun Qian, Lingling Wang.

simple electric monitoring with Wemos D1, MQTT, plotly dash and SQLAlchemy.

Examples of scheduled jobs estimating copulas at www.microprediction.org

Python package for receiving and restructuring OSM historic object data conveniently

🔁Graphical models, Recurrent Neural Networks and SIFT algorithm for image processing, signal analysis and timeseries forecasting (MD Course: Intelligent Systems for Pattern Recognition)

Generate time series from any AR and MA specification, estimate parameters for custom ARMA models using a Pymc3 wrapper.

A lib for easier ML tasks

Supporting community nowcasts at www.microprediction.org

Code release of ODToolkit: An Extensible Toolkit for Occupancy Detection