404 Open source projects that are alternatives of or similar to ctsa

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The Chronix Server implementation that is based on Apache Solr.

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Calculates various features from time series data. Python implementation of the R package tsfeatures.

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An interactive 3D animation using WebGL to depict a 2D predator prey ecology on a grid real-time mapped onto the surface of a 3D torus. Sound file is parsed then visualized both in time and frequency domains as well as rendered using Web Audio API - this is an exercise where I taught myself how to display data for an ongoing project on sound syn…

An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.

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List of papers, code and experiments using deep learning for time series forecasting

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Deep learning meets molecular dynamics.

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A python multi-variate time series prediction library working with sklearn

Realtime animated graphs with PubNub and C3.

This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the time series forecasting research space.

⏰ This R package provides the tools to perform standard and robust wavelet variance analysis for time series (signal processing). Among others, aside from computing the wavelet variance and cross-covariance (classic and robust), the package provides inference tools (e.g. confidence intervals) and plotting tools allowing to perform some visual an…

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An Open Source Distributed Time Series Database with high performance, high compression ratio and high usability.

THIS REPO HAS MOVED TO https://github.com/apache/incubator-iotdb. TsFile is a columnar file format designed for time-series data, which supports efficient compression and query. It is easy to integrate TsFile with your IOT big data processing frameworks.

An "R" package for automatic download and preprocessing of MODIS Land Products Time Series

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framework for developing machine (and deep) learning models for structured data

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Analyzes chronological patterns present in time-series data and provides human-readable descriptions

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