148 Open source projects that are alternatives of or similar to Sax Vsm_classic

Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai

Anomaly detection

Timeseries database

Time series distances: Dynamic Time Warping (DTW)

Calculates the Hurst exponent of a time series based on Rescaled range (R/S) analysis.

Plotting and logging of real-time data for desktop.

Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.

Time series based anomaly detector

Tools for time series analysis and forecasting

📈 A small, fast chart for time series, lines, areas, ohlc & bars

Python implementation of KNN and DTW classification algorithm

A Julia package for estimating ARMA-GARCH models.

TensorFlow 1.3 experiment with LSTM (and GRU) RNNs for sine prediction

Chart.js module for charting financial securities

ReactJS component for FusionCharts JavaScript Charting library.

A toolkit for working with time series in R

Solution to Kaggle - Web Traffic Time Series Forecasting

Fast data store for Pandas time-series data

Kibana Alert & Report App for Elasticsearch

Hastic data management server for analyzing patterns and anomalies from Grafana

📈 The InfluxDB Client for Node.js and Browsers

Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.

Distributed "massively parallel" SQL query engine

RedisTimeSeries python client

Golang implementation of Graphite/Carbon server with classic architecture: Agent -> Cache -> Persister

Bringing financial analysis to the tidyverse

Hastic standalone

A tool for generating random, yet realistic, time series values

Time Series data structure for Redis

Java implementation of Seasonal-Trend-Loess time-series decomposition algorithm.

Codebase for the paper LSTM Fully Convolutional Networks for Time Series Classification

This is a times series anomaly detection algorithm, implemented in Python, for catching multiple anomalies. It uses a moving average with an extreme student deviate (ESD) test to detect anomalous points.

SiriDB is a highly-scalable, robust and super fast time series database. Build from the ground up SiriDB uses a unique mechanism to operate without a global index and allows server resources to be added on the fly. SiriDB's unique query language includes dynamic grouping of time series for easy analysis over large amounts of time series.

Python driver for MarketStore

A python library for Bayesian time series modeling

Interpolation-Prediction Networks for Irregularly Sampled Time Series

Cloudinsight Agent is a system tool that monitors system processes and services, and sends information back to your Cloudinsight account.

QoS Measurement & Dependency Graph Platform

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

A Time Series Library for Apache Spark

Multivariate LSTM Fully Convolutional Networks for Time Series Classification

Durable time-series database that's API-compatible with Prometheus.

Portfolio optimization with deep learning.

A Python package for time series classification

Synthetic structured data generators

NeuralProphet - A simple forecasting model based on Neural Networks in PyTorch

Interactive visualizations of time series using JavaScript and the HTML canvas tag

An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana

Timeseries database

Apache IoTDB

Lightweight, functional and correct time-series library for scala. Easy manipulation, filtering and combination of time-series data.

Performance Co-Pilot

Java client libraries for redis-modules https://redis.io/modules, based on Redisson. https://github.com/redisson/redisson

Time series data visualization software

Declarative and modular timeseries charting components for React

Graphite InfluxDB backend. InfluxDB storage finder / plugin for Graphite API.

R interface to fast.ai

Java implementation of SAX, HOT-SAX, and EMMA

Go Share your TimeSeries/NameSpace/KeyVal DataStore (using leveldb) over HTTP &/or ZeroMQ

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.