A Python package for time series classification

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

Anomaly detection

ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python

golang library for computing matrix profiles along with other time series analysis features

Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.

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

trend / momentum and other patterns in financial timeseries

Investigate how mutual funds leverage credit derivatives by studying their routine filings to the SEC using NLP techniques 📈🤑

Python based Quant Finance Models, Tools and Algorithmic Decision Making

Scripts to facilitate parallel InSAR processing and analysis of Sentinel-1 time series on HPC clusters based on GMTSAR and Slurm.

Hybrid Time Series using LSTM and Kalman Filtering

My personal notes for Udacity's AI for Trading Nanodegree

Jupyter Notebooks Collection for Learning Time Series Models

A repository to compare the performance between the algorithms implemented in pyts and the performance reported in the literature

Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.

Client for Microprediction.Org

Ingest sample Market Orders Data feed from PubNub to Postgres with TimescaleDB extension installed and enabled for time series analysis.

Predication of stock market price using different machine learning models

Artificial Neural Networks in Economic Forecasting (ANNEF)

A package for time series classification in Weka.

Time Series Analysis Concepts Explained with examples

Performant, composable online learning

Predicting user movements from temporal streams of RSS (Radio Signal Strength) measured between the nodes of a WSN (Wireless Sensor Network WSN)

Udacity Deep Learning Nanodegree Project 1 on bike-sharing patterns. Do 🌟 it and show some love

Exploring HMM, LSTM and Regression techniques to predict respiratory rate of an individual from accelerometer data.

The exploratory data analysis for the road traffic data-sets used in the Traffic Congestion project.

Multi-Scale Entropy (SampEn) analysis tool