timeseries-analysis

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

Continuously evaluated, pure-functional, time-series forecastsing methods

The purpose of this project is to provide an API for manipulating time series on top of Apache Spark. Functionality includes featurization using lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, and downsampling & interpolation. This has been tested on TB-scale of historical data and is unit tested for quality purposes.

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

trend / momentum and other patterns in financial timeseries

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

Client for Microprediction.Org

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

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

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

Jupyter Notebooks Collection for Learning Time Series Models

Python based Quant Finance Models, Tools and Algorithmic Decision Making

Hybrid Time Series using LSTM and Kalman Filtering

Repository for the theory module TDT99

My personal notes for Udacity's AI for Trading Nanodegree

Hello world univariate examples for a variety of time series packages.

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

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

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

interpretable gaussian processes for stellar light curves

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

Performant, composable online learning