Displaying 1 to 20 from 65 results

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

  •    Javascript

Learn more about it at dygraphs.com. Get help with dygraphs by browsing the on Stack Overflow (preferred) and Google Groups.

m3 - M3 monorepo - Distributed TSDB and Query Engine, Prometheus Sidecar, Metrics Platform

  •    Go

Distributed TSDB and Query Engine, Prometheus Sidecar, Metrics Aggregator, and more. (For a fully comprehsensive getting started guide, see our single node how-to).

facette - Time series data visualization software

  •    Go

Facette is a open source web application to display time series data from various sources — such as collectd, Graphite, InfluxDB or KairosDB — on graphs. To learn more on its architecture, read this page. The source code is available at Github and is licensed under the terms of the BSD license.




Pandas - Powerful Python Data Analysis Toolkit

  •    Python

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions. It supports aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets, High performance merging and joining of data sets, Time series-functionality, Hierarchical axis indexing and lot more.

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

  •    Go

Faster than default carbon. In all conditions :) How much faster depends on server hardware, storage-schemas, etc. There were some efforts to find out maximum possible performance of go-carbon on a hardware (2xE5-2620v3, 128GB RAM, local SSDs).

EventQL - The database for large-scale event analytics

  •    C++

EventQL is a distributed, column-oriented database built for large-scale event collection and analytics. It runs super-fast SQL and MapReduce queries. Its features include Automatic partitioning, Columnar storage, Standard SQL support, Scales to petabytes, Timeseries and relational data, Fast range scans and lot more.

node-influx - πŸ“ˆ The InfluxDB Client for Node.js and Browsers

  •    TypeScript

For browsers, see the browser setup instructions. Version 3.x.x is compatible with InfluxDB 0.8.x - 3.x will no longer have updates by core contributers, please consider upgrading.


Marketstore - DataFrame Server for Financial Timeseries Data

  •    Go

MarketStore is a database server optimized for financial timeseries data. You can think of it as an extensible DataFrame service that is accessible from anywhere in your system, at higher scalability. It is designed from the ground up to address scalability issues around handling large amounts of financial market data used in algorithmic trading backtesting, charting, and analyzing price history with data spanning many years, including tick-level for the all US equities or the exploding crypto currencies space. If you are struggling with managing lots of HDF5 files, this is perfect solution to your problem.

Griddb - High performance, High scalability and High reliability database for big data

  •    C++

GridDB is an In-Memory NoSQL Database for highly scalable IoT applications . It has a KVS (Key-Value Store)-type data model that is suitable for sensor data stored in a timeseries. It is a database that can be easily scaled-out according to the number of sensors. High Reliability It is equipped with a structure to spread out the replication of key value data among fellow nodes so that in the event of a node failure, automatic failover can be carried out in a matter of seconds by using the replication function of other nodes.

flint - A Time Series Library for Apache Spark

  •    Scala

The ability to analyze time series data at scale is critical for the success of finance and IoT applications based on Spark. Flint is Two Sigma's implementation of highly optimized time series operations in Spark. It performs truly parallel and rich analyses on time series data by taking advantage of the natural ordering in time series data to provide locality-based optimizations. Flint is an open source library for Spark based around the TimeSeriesRDD, a time series aware data structure, and a collection of time series utility and analysis functions that use TimeSeriesRDDs. Unlike DataFrame and Dataset, Flint's TimeSeriesRDDs can leverage the existing ordering properties of datasets at rest and the fact that almost all data manipulations and analysis over these datasets respect their temporal ordering properties. It differs from other time series efforts in Spark in its ability to efficiently compute across panel data or on large scale high frequency data.

react-timeseries-charts - Declarative and modular timeseries charting components for React

  •    Javascript

This library contains a set of modular charting components used for building flexible interactive charts. It was built for React from the ground up, specifically to visualize timeseries data and network traffic data in particular. Low level elements are constructed using d3, while higher level composability is provided by React. Charts can be stacked as rows, overlaid on top of each other, or any combination, all in a highly declarative manner. The library is used throughout the public facing ESnet Portal.

siridb-server - SiriDB is a highly-scalable, robust and super fast time series database

  •    C

SiriDB is a highly-scalable, robust and super fast time series database. For Ubuntu we have a deb package available which can be downloaded here.

K-Nearest-Neighbors-with-Dynamic-Time-Warping - Python implementation of KNN and DTW classification algorithm

  •    Jupyter

When it comes to building a classification algorithm, analysts have a broad range of open source options to choose from. However, for time series classification, there are less out-of-the box solutions. I began researching the domain of time series classification and was intrigued by a recommended technique called K Nearest Neighbors and Dynamic Time Warping. A meta analysis completed by Mitsa (2010) suggests that when it comes to timeseries classification, 1 Nearest Neighbor (K=1) and Dynamic Timewarping is very difficult to beat [1].

Cronos

  •    

This is a complete time series analysis package written in C#. It provides a number of tools for data manipulation, and supports a range of different models, including ARMA and GARCH models. A plugin framework allows developers to create their own custom models and transforms.

Time-series Framework

  •    

Core framework used to manage, process and respond to dynamic changes in fast moving streaming time-series data in real-time.

Gnocchi - Time series database

  •    Python

Gnocchi is an open-source |time series| database. The problem that Gnocchi solves is the storage and indexing of |time series| data and resources at a large scale. This is useful in modern cloud platforms which are not only huge but also are dynamic and potentially multi-tenant. Gnocchi takes all of that into account. Gnocchi has been designed to handle large amounts of aggregates being stored while being performant, scalable and fault-tolerant. While doing this, the goal was to be sure to not build any hard dependency on any complex storage system.

tidyquant - Bringing financial analysis to the tidyverse

  •    R

tidyquant integrates the best resources for collecting and analyzing financial data, zoo, xts, quantmod, TTR, and PerformanceAnalytics, with the tidy data infrastructure of the tidyverse allowing for seamless interaction between each. You can now perform complete financial analyses in the tidyverse. Our short introduction to tidyquant on YouTube.

metrics - Metrics Query Engine

  •    Go

Metrics Query Engine(MQE) provides SQL-like interface to time series data with powerful functions to aggregate, filter and analyze.Or you want to see how many cumulative seconds have been spent serving an API request.

tensorflow-lstm-sin - TensorFlow 1.3 experiment with LSTM (and GRU) RNNs for sine prediction

  •    Python

Single- and multilayer LSTM networks with no additional output nonlinearity based on aymericdamien's TensorFlow examples and Sequence prediction using recurrent neural networks. Experiments with varying numbers of hidden units, LSTM cells and techniques like gradient clipping were conducted using static_rnn and dynamic_rnn. All networks have been optimized using Adam on the MSE loss function.