timeseries-analysis

Statsmodels: statistical modeling and econometrics in Python

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

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

GRASS GIS - free and open source Geographic Information System (GIS)

Anomaly detection

Continuously evaluated, functional, incremental, time-series forecasting

Python implementation of k-Shape

API for manipulating time series on top of Apache Spark: lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, downsampling, and interpolation

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

A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.

Timeseries Anomaly detection and Root Cause Analysis on data in SQL data warehouses and databases

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

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

trend / momentum and other patterns in financial timeseries

If you can measure it, consider it predicted

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

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

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

BangDB - nosql database

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