Search Results for "time series analisys" - Page 2
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Showing 369 open source projects for "time series analisys"
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SigLens
100x Efficient Log Management than Splunk
Siglens is an open-source signal analysis toolkit designed for processing and visualizing time-series data, commonly used in scientific and engineering applications. -
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Explorer
Series (one-dimensional) and dataframes (two-dimensional)
Explorer brings series (one-dimensional) and data frames (two-dimensional) to Elixir for fast data exploration. -
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StatsForecast
Fast forecasting with statistical and econometric models
StatsForecast is a Python library for time-series forecasting that delivers a suite of classical statistical and econometric forecasting models optimized for high performance and scalability. It is designed not just for academic experiments but for production-level time-series forecasting, meaning it handles forecasting for many series at once, efficiently, reliably, and with minimal overhead. -
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Nixtla TimeGPT
TimeGPT-1: production ready pre-trained Time Series Foundation Model
...A generative model for time series. TimeGPT is capable of accurately predicting various domains such as retail, electricity, finance, and IoT. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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YData Synthetic
Synthetic data generators for tabular and time-series data
A package to generate synthetic tabular and time-series data leveraging state-of-the-art generative models. Synthetic data is artificially generated data that is not collected from real-world events. It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy. This repository contains material related to Generative Adversarial Networks for synthetic data generation, in particular regular tabular data and time-series. ... -
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Orion
A machine learning library for detecting anomalies in signals
Orion is a machine-learning library built for unsupervised time series anomaly detection. Such signals are generated by a wide variety of systems, few examples include telemetry data generated by satellites, signals from wind turbines, and even stock market price tickers. We built this to provide one place where users can find the latest and greatest in machine learning and deep learning world including our own innovations. -
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forecast
Forecasting Functions for Time Series and Linear Models
The forecast package is a comprehensive R package for time series analysis and forecasting. It provides functions for building, assessing, and using univariate forecasting models (e.g. ARIMA, exponential smoothing, etc.), tools for automatic model selection, diagnostics, plotting, forecasting future values, etc. It's widely used in statistics, economics, business forecasting, environmental science, etc. -
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Trafexia
Mobile Traffic Interceptor
Trafexia is a lightweight web application designed to visualize real-time and historical transportation data, such as traffic flows, transit schedules, or route usage, with clean dashboards and map overlays that help users and analysts interpret mobility patterns quickly. It integrates data streams from APIs, CSV feeds, or sensor inputs and processes them into intuitive visualizations including flow diagrams, heatmaps, time series charts, and route performance boards. ... -
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MarketData.jl
Time series market data
The MarketData package provides open-source financial data for research and testing. The data is from Quandl and is free end-of-day stock data. It is public domain without restrictions. The TimeSeries TimeArray data structure is used to store the data, but conversion to other data structures, including DataFrames and AxisArrays, is supported. -
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VictoriaMetrics
VictoriaMetrics: fast, cost-effective monitoring solution
VictoriaMetrics is a fast, cost-effective and scalable monitoring solution and time series database. VictoriaMetrics is available in binary releases, Docker images, Snap packages and source code. Just download the latest version of VictoriaMetrics. VictoriaMetrics is developed at a fast pace, so it is recommended periodically checking the CHANGELOG and performing regular upgrades. It can be used as long-term storage for Prometheus. -
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GluonTS
Probabilistic time series modeling in Python
GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models. GluonTS requires Python 3.6 or newer, and the easiest way to install it is via pip. We train a DeepAR-model and make predictions using the simple "airpassengers" dataset. The dataset consists of a single time-series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months). -
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CrateDB
CrateDB is a distributed and scalable SQL database
CrateDB is a distributed SQL database designed for massive machine data and real-time analytics. It combines the scalability and performance of NoSQL with the power and simplicity of SQL, allowing for horizontal scaling, full-text search, and complex queries over large datasets. Built in Java and powered by Elasticsearch and Lucene, CrateDB is optimized for high-velocity data ingestion and dynamic queries. -
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TimesFM
Pretrained time-series foundation model developed by Google Research
TimesFM is a pretrained time-series foundation model from Google Research built for forecasting tasks, designed to generalize across many domains without requiring extensive per-dataset retraining. It provides a decoder-only model approach to forecasting, aiming for strong performance even in zero-shot or low-data settings where traditional models often struggle. -
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Stoolap
A Modern Embedded SQL Database written in Rust
Stoolap is a lightweight, self-hosted analysis and visualization tool designed to help developers and operations teams explore log data, metrics, and debugging information from distributed systems or local applications. Instead of relying on heavy commercial observability platforms, stoolap provides a fast, focused interface where users can filter, query, and visualize time-series data, logs, traces, and error metrics in a cohesive environment. Its intuitive query builder lets users craft powerful filters and aggregations without complex configurations, making it easier to diagnose performance bottlenecks or uncover unusual events during testing and production. Stoolap supports dashboards, alert triggering, and exports so teams can embed visualizations in reports or monitor critical thresholds in real time. -
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Rerun
Visualize streams of multimodal data
Rerun is an open-source tool that helps developers visualize real-time multimodal data streams, such as images, point clouds, and tensors, for debugging and understanding ML and robotics systems. Designed for use with Python and Rust, it captures logged data and renders it through an interactive desktop interface, making it easier to understand how complex systems behave over time. -
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Grafana Mimir
Grafana Mimir provides long-term storage for Prometheus
Grafana Mimir is an open-source, horizontally scalable, long-term storage solution for Prometheus metrics. Built by Grafana Labs, Mimir is designed to handle massive volumes of time-series data efficiently while maintaining high availability and reliability. It enables organizations to scale their Prometheus infrastructure without the typical limitations of single-server setups. Mimir is used to power Grafana Cloud Metrics and is built to be fully compatible with Prometheus, allowing easy integration into existing monitoring workflows. -
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QuestDB
An open source SQL database designed to process time series data
...It includes endpoints for PostgreSQL wire protocol, high-throughput schema-agnostic ingestion using InfluxDB Line Protocol, and a REST API for queries, bulk imports, and exports. QuestDB implements ANSI SQL with native extensions for time-oriented language features. These extensions make it simple to correlate data from multiple sources using relational and time series joins. QuestDB achieves high performance from a column-oriented storage model, massively-parallelized vector execution, SIMD instructions, and various low-latency techniques. The entire codebase was built from the ground up in Java and C++, with no dependencies, and is 100% free from garbage collection. ... -
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Label Studio
Label Studio is a multi-type data labeling and annotation tool
...Configurable label formats let you customize the visual interface to meet your specific labeling needs. Support for multiple data types including images, audio, text, HTML, time-series, and video. -
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collapse
Advanced and Fast Data Transformation in R
...It operates on base R data structures like data frames and vectors and uses highly optimized C++ code under the hood to deliver significant speed improvements. collapse also includes tools for grouped operations, weighted statistics, and time series manipulation, making it a compact yet powerful utility for data scientists and researchers working in R. -
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Frankfurter
Currency data API
...It is designed to be simple, transparent, and free to use, with no API key requirements or usage limits, making it particularly attractive for developers and small projects. The API offers endpoints for retrieving the latest exchange rates, historical data, and time-series data, enabling both basic conversions and more advanced financial analysis. It allows users to specify base currencies and filter results by selected currencies, making responses efficient and customizable. The system can be used directly from client-side applications or self-hosted for full control over data access. Its lightweight design and straightforward JSON responses make integration easy across web and mobile environments. -
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Anomaly Detection Learning Resources
Anomaly detection related books, papers, videos, and toolboxes
...The repository organizes resources into structured categories such as books, tutorials, academic papers, datasets, benchmark frameworks, and open-source toolkits. It includes materials covering a wide range of anomaly detection domains, including time series data, graph data, tabular datasets, and real-time monitoring systems. By compiling resources from multiple programming ecosystems such as Python, R, and other machine learning platforms, the repository allows users to discover both research papers and practical implementations. -
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tsai
Time series Timeseries Deep Learning Machine Learning Pytorch fastai
tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series tasks like classification, regression, forecasting, and imputation. Starting with tsai 0.3.0 tsai will only install hard dependencies. Other soft dependencies (which are only required for selected tasks) will not be installed by default (this is the recommended approach. If you require any of the dependencies that is not installed, tsai will ask you to install it when necessary) We've also added a new PredictionDynamics callback that will display the predictions during training. ... -
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KCloud‑Platform‑IoT
KCloud-Platform-IoT
KCloud-Platform-IoT is a comprehensive open-source IoT management platform built with Spring Cloud and Vue.js. It supports device registration, data collection, rule-based processing, and dashboard visualization. Designed for scalability and modularity, the platform is ideal for managing large IoT fleets in industrial or smart city environments. -
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Norfair
Lightweight Python library for adding real-time multi-object tracking
Norfair is a customizable lightweight Python library for real-time multi-object tracking. Using Norfair, you can add tracking capabilities to any detector with just a few lines of code. Any detector expressing its detections as a series of (x, y) coordinates can be used with Norfair. This includes detectors performing tasks such as object or keypoint detection. It can easily be inserted into complex video processing pipelines to add tracking to existing projects. -
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FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
FinRobot is an open-source AI framework focused on automating financial data workflows by combining data ingestion, feature engineering, model training, and automated decision-making pipelines tailored for quantitative finance applications. It provides developers and quants with structured modules to fetch market data, process time series, generate technical indicators, and construct features appropriate for machine learning models, while also supporting backtesting and evaluation metrics to measure strategy performance. Built with modularity in mind, FinRobot allows users to plug in custom models — from classical algorithms to deep learning architectures — and orchestrate components in pipelines that can run reproducibly across experiments. ...
