# Time-Series Forecasting Machine Learning

> Search results for `forecasting time-series data with machine learning` on awesome-repositories.com. 116 total matches; showing the first 50.

Explore on the web: https://awesome-repositories.com/q/forecasting-time-series-data-with-machine-learning

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## Results

- [kamranahmedse/developer-roadmap](https://awesome-repositories.com/repository/kamranahmedse-developer-roadmap.md) (357,434 ⭐) — Developer Roadmap is a community-driven platform that provides structured, graph-based learning paths for software engineering. It serves as a comprehensive knowledge repository where technical domains are organized into visual sequences to guide professional skill acquisition and career growth.

The project distinguishes itself through a collaborative ecosystem that enables users to contribute roadmaps, curate industry best practices, and maintain professional profiles. It integrates diagnostic assessment frameworks to evaluate technical proficiency, helping developers identify knowledge gaps
- [google-research/google-research](https://awesome-repositories.com/repository/google-research-google-research.md) (38,139 ⭐) — This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development.

The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed
- [rhiever/data-analysis-and-machine-learning-projects](https://awesome-repositories.com/repository/rhiever-data-analysis-and-machine-learning-projects.md) (6,699 ⭐) — This is a collection of machine learning projects, data visualization portfolios, and predictive analytics tools. The repository provides implementation examples for training predictive models, executing data analysis pipelines, and estimating metadata values through historical statistical tables.

The project emphasizes evolutionary computing, utilizing genetic algorithms and programming to solve optimization problems. This includes calculating the shortest distance between geographic coordinates and automating the selection of models and hyperparameters within machine learning pipelines.

Ad
- [nyandwi/machine_learning_complete](https://awesome-repositories.com/repository/nyandwi-machine-learning-complete.md) (4,983 ⭐) — This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi
- [ustc-time-series/tokencast](https://awesome-repositories.com/repository/ustc-time-series-tokencast.md) (25 ⭐) — TokenCast: An LLM-Driven Framework for Context-Aware Time Series Forecasting via Symbolic Discretization
- [jakevdp/pythondatasciencehandbook](https://awesome-repositories.com/repository/jakevdp-pythondatasciencehandbook.md) (48,561 ⭐) — This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping.

The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st
- [eugeneyan/applied-ml](https://awesome-repositories.com/repository/eugeneyan-applied-ml.md) (29,783 ⭐) — This project is a comprehensive, curated knowledge base designed to support the development and maintenance of production-grade machine learning systems. It serves as a centralized repository of industry-standard technical literature, engineering case studies, and research papers, providing a structured reference for practitioners navigating the complexities of modern data science and machine learning engineering.

The resource distinguishes itself through a cross-domain approach that bridges the gap between academic research and practical implementation. By synthesizing proven industry archit
- [voltagent/awesome-claude-code-subagents](https://awesome-repositories.com/repository/voltagent-awesome-claude-code-subagents.md) (21,906 ⭐) — This project provides a framework for managing multi-agent systems, designed to automate complex software development, infrastructure, and business workflows. It functions as a multi-agent workflow orchestrator that routes tasks to domain-specific workers while maintaining state persistence and infrastructure automation. By leveraging large language models, the system decomposes high-level objectives into actionable plans, ensuring that complex operations are executed with consistency and reliability.

The framework distinguishes itself through its hierarchical agent registry and policy-driven
- [apple/foundationdb](https://awesome-repositories.com/repository/apple-foundationdb.md) (16,446 ⭐) — FoundationDB is an ACID-compliant distributed transactional key-value store. It functions as a scalable database engine that ensures strict serializability and data consistency across a cluster of servers using a shared-nothing architecture.

The system is distinguished by its multi-region replication capabilities, allowing data to be synchronized across different datacenters for high availability and disaster recovery. It utilizes optimistic concurrency control to manage distributed transactions and employs a majority-based coordination system to maintain cluster state.

The platform provides
- [susanli2016/machine-learning-with-python](https://awesome-repositories.com/repository/susanli2016-machine-learning-with-python.md) (4,583 ⭐) — Python codes for common Machine Learning Algorithms
- [arbox/machine-learning-with-ruby](https://awesome-repositories.com/repository/arbox-machine-learning-with-ruby.md) (2,215 ⭐) — Curated list: Resources for machine learning in Ruby
- [ashishpatel26/500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code](https://awesome-repositories.com/repository/ashishpatel26-500-ai-machine-learning-deep-learning-computer-vision-nlp-projects.md) (34,579 ⭐) — This repository serves as a comprehensive, curated collection of open-source implementations focused on artificial intelligence, machine learning, and computer vision. It functions as a centralized knowledge base and technical resource index, providing students and professional engineers with a structured directory of code examples for educational and practical reference.

The project distinguishes itself through a community-driven curation model, relying on manual updates and contributions to maintain a relevant and expansive archive. By organizing these resources into categorized lists, the
- [statsmodels/statsmodels](https://awesome-repositories.com/repository/statsmodels-statsmodels.md) (11,260 ⭐) — Statsmodels is a comprehensive Python library designed for statistical modeling, econometric research, and data analysis. It provides a robust framework for estimating and diagnosing a wide range of statistical models, enabling users to perform rigorous hypothesis testing, regression analysis, and complex data exploration within structured environments.

The library distinguishes itself through its support for advanced statistical methodologies, including state space representation for dynamic systems and generalized linear frameworks that accommodate non-normal response variables. It offers s
- [vmware/data-annotator-for-machine-learning](https://awesome-repositories.com/repository/vmware-data-annotator-for-machine-learning.md) (61 ⭐) — Data Annotator for Machine Learning
- [clickhouse/clickhouse](https://awesome-repositories.com/repository/clickhouse-clickhouse.md) (48,229 ⭐) — ClickHouse is a high-performance, columnar analytical database designed for real-time query execution and large-scale data aggregation. It functions as a distributed data warehouse capable of processing petabytes of information, while also providing an embedded engine that integrates directly into applications for native query capabilities without external dependencies. The system is built to handle high-throughput ingestion and complex analytical workloads, delivering millisecond-level latency for interactive dashboards and operational monitoring.

The platform distinguishes itself through ad
- [facebookincubator/prophet](https://awesome-repositories.com/repository/facebookincubator-prophet.md) (20,231 ⭐) — Prophet is a predictive analytics framework and time series regression library designed for forecasting future values. It uses additive models to fit non-linear growth and periodic seasonal patterns, providing tools for producing forecasts with integrated error measurement.

The project handles multiple seasonalities and holiday effects to improve accuracy for periodic data. It supports the integration of external regressors and manages data irregularities, such as missing data and outliers, to maintain prediction stability.

The framework covers a broad range of analysis capabilities, includi
- [letianzj/quantresearch](https://awesome-repositories.com/repository/letianzj-quantresearch.md) (2,808 ⭐) — QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics.

The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statisti
- [thuml/time-series-library](https://awesome-repositories.com/repository/thuml-time-series-library.md) (12,494 ⭐) — TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.
- [shiyu-coder/kronos](https://awesome-repositories.com/repository/shiyu-coder-kronos.md) (30,502 ⭐) — Kronos is a financial time-series forecasting framework and quantitative trading strategy simulator. It functions as a research environment designed to analyze historical market data, train predictive models, and evaluate the performance of automated trading signals.

The platform distinguishes itself through its deep learning sequence predictors and probabilistic market modeling tools. By utilizing sequence-based architectures and statistical sampling, the system generates multiple potential price trajectories and volatility estimates to quantify uncertainty. It also supports transfer learnin
- [crowdcurio/time-series-annotator](https://awesome-repositories.com/repository/crowdcurio-time-series-annotator.md) (59 ⭐) — Time series annotation library.
- [humansignal/label-studio](https://awesome-repositories.com/repository/humansignal-label-studio.md) (27,619 ⭐) — Label Studio is a multi-modal data annotation platform designed to create and manage high-quality training datasets for machine learning. It functions as a self-hosted, containerized environment that supports secure, private deployments, including air-gapped configurations. The platform provides a centralized workspace for labeling diverse media types, such as images, text, audio, and time-series data, to support supervised and reinforcement learning workflows.

The platform distinguishes itself through deep integration with machine learning backends, enabling active learning loops, automated
- [kutaytire/retrieval-augmented-time-series-forecasting](https://awesome-repositories.com/repository/kutaytire-retrieval-augmented-time-series-forecasting.md) (58 ⭐) — To perform inference for RAF and the baseline, install the necessary packages by running:
- [ludwig-ai/ludwig](https://awesome-repositories.com/repository/ludwig-ai-ludwig.md) (11,717 ⭐) — Ludwig is a multimodal machine learning platform and low-code framework designed for building, training, and deploying neural networks. It enables the construction of models that process text, images, audio, and tabular data through a unified interface using declarative configuration files rather than custom code.

The system features a specialized low-code framework for large language models, supporting supervised fine-tuning, preference alignment, and a constrained decoding tool to force structured data output via logit extraction. It also includes an automated model architecture search to i
- [plotly/plotly.py](https://awesome-repositories.com/repository/plotly-plotly-py.md) (18,270 ⭐) — Plotly.py is a comprehensive framework for building production-ready data applications and interactive dashboards directly from Python code. It functions as both a high-performance visualization library for browser-based charts and a full-stack tool for transforming analytical scripts into responsive, web-based interfaces. By abstracting away the need for manual HTML or JavaScript, it allows developers to define complex layouts and functional logic using modular, reusable components.

The framework distinguishes itself through a robust architecture that handles event orchestration and state sy
- [humphd/have-fun-with-machine-learning](https://awesome-repositories.com/repository/humphd-have-fun-with-machine-learning.md) (5,110 ⭐) — An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks
- [kedacore/keda](https://awesome-repositories.com/repository/kedacore-keda.md) (10,314 ⭐) — KEDA is a Kubernetes event-driven autoscaler and cloud event scaling engine. It functions as a custom metrics provider that monitors external event sources—including message brokers, databases, and cloud metrics—to dynamically adjust the replica counts of containerized workloads.

The project is distinguished by its scale-to-zero workflow, which reduces workloads to zero replicas during inactivity and automatically restarts them when new events are detected. It operates as a multi-cloud event trigger system, using a pluggable scaler interface to integrate with a wide array of third-party servi
- [microsoft/qlib](https://awesome-repositories.com/repository/microsoft-qlib.md) (44,490 ⭐) — This project is a comprehensive platform for quantitative investment research, machine learning, and algorithmic trading. It provides an end-to-end environment for developing, testing, and executing financial strategies, supporting the entire lifecycle from data ingestion and feature engineering to model training and backtesting.

The system is distinguished by its configuration-driven workflow orchestration, which allows researchers to automate complex pipelines and manage experiments through declarative files. It features a high-performance data infrastructure that utilizes custom binary for
- [samcohen16/aligning-time-series](https://awesome-repositories.com/repository/samcohen16-aligning-time-series.md) (50 ⭐) — This repository consists of an implementation of the Gromov-DTW metric for time series living on incomparable spaces
- [josephmisiti/awesome-machine-learning](https://awesome-repositories.com/repository/josephmisiti-awesome-machine-learning.md) (72,867 ⭐) — This project is a comprehensive, community-driven directory of machine learning resources, software libraries, and educational materials. It serves as a centralized knowledge base for developers and researchers, organizing tools and frameworks by their primary programming language and technical domain to simplify discovery across the artificial intelligence ecosystem.

The collection distinguishes itself by providing a cross-language development index that spans diverse programming environments, including C, C++, Rust, Clojure, and Python. It covers a wide range of specialized capabilities, fr
- [qingsongedu/time-series-transformers-review](https://awesome-repositories.com/repository/qingsongedu-time-series-transformers-review.md) (2,990 ⭐) — A professionally curated list of awesome resources (paper, code, data, etc.) on Transformers in Time Series, which is first work to comprehensively and systematically summarize the recent advances of Transformers for modeling time series data to the best of our knowledge.
- [dragonflydb/dragonfly](https://awesome-repositories.com/repository/dragonflydb-dragonfly.md) (30,688 ⭐) — Dragonfly is a high-performance, multi-model in-memory data store designed to serve as a drop-in replacement for existing database infrastructures. By utilizing a multi-threaded, shared-nothing architecture and a fiber-based concurrency model, it maximizes CPU utilization and minimizes latency for read and write operations. The system supports a wide range of data structures, including strings, hashes, lists, sets, sorted sets, and JSON documents, while maintaining full compatibility with standard industry wire protocols and client libraries.

What distinguishes Dragonfly is its focus on effic
- [wilsonfreitas/awesome-quant](https://awesome-repositories.com/repository/wilsonfreitas-awesome-quant.md) (26,818 ⭐) — Awesome-quant is a curated directory of open-source software libraries and tools designed for quantitative finance, algorithmic trading, and financial data analysis. It serves as a central hub for discovering resources that support the entire lifecycle of financial modeling, from raw data ingestion to complex statistical research.

The repository organizes specialized tools into categorized collections, enabling users to identify solutions for high-performance numerical computing, technical indicator calculation, and derivative pricing. It highlights frameworks that facilitate the construction
- [mysql/mysql-server](https://awesome-repositories.com/repository/mysql-mysql-server.md) (12,297 ⭐) — MySQL Server is a relational database management system designed to organize and store structured information. It functions as a comprehensive SQL server platform that provides reliable transactional integrity and high-performance query execution for enterprise data management.

The system distinguishes itself through a pluggable storage engine architecture that decouples logical query processing from physical data storage, allowing for specialized handling of diverse workloads. It maintains data consistency and high concurrency through multi-version concurrency control and write-ahead logging
- [ermshaua/time-series-segmentation-benchmark](https://awesome-repositories.com/repository/ermshaua-time-series-segmentation-benchmark.md) (84 ⭐) — The problem of time series segmentation (TSS) is to find a meaningful segmentation of a time series (TS) that captures a data-generating process with distinct states and transitions. We consider a segmentation meaningful, if the change points (CPs) between two consecutive segments correspond to…
- [jack-cherish/machine-learning](https://awesome-repositories.com/repository/jack-cherish-machine-learning.md) (10,333 ⭐) — This project is a collection of supervised and unsupervised machine learning algorithms implemented from scratch using Python. It serves as an educational resource for studying model training, parameter optimization, and the implementation of core predictive models.

The library provides a variety of supervised learning tools, including linear and logistic regression, decision trees, and support vector machines. It also features unsupervised learning capabilities for discovering patterns in unlabeled datasets through clustering algorithms.

Broad capability areas include ensemble learning thro
- [aistream-peelout/flow-forecast](https://awesome-repositories.com/repository/aistream-peelout-flow-forecast.md) (2,285 ⭐) — Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
- [avelino/awesome-go](https://awesome-repositories.com/repository/avelino-awesome-go.md) (175,576 ⭐) — This project serves as a comprehensive language ecosystem index, functioning as a centralized, community-curated directory for the Go programming language. It organizes a vast landscape of software components, libraries, and development tools into a structured, navigable hierarchy, enabling developers to efficiently discover resources tailored to specific functional domains.

The repository distinguishes itself through a decentralized contribution model, where community-driven updates ensure the index remains current with the rapidly evolving software landscape. Beyond simple resource listing,
- [aladdinpersson/machine-learning-collection](https://awesome-repositories.com/repository/aladdinpersson-machine-learning-collection.md) (8,465 ⭐) — This project is a machine learning educational repository providing a collection of implementations and guides for machine learning and deep learning algorithms. It serves as a deep learning model library and a reference for training workflows, covering foundational machine learning, convolutional, recurrent, and transformer architectures.

The collection includes a generative adversarial network suite for synthesizing realistic images and performing image-to-image translation. It also functions as a computer vision implementation guide for object detection and semantic segmentation, alongside
- [arangodb/arangodb](https://awesome-repositories.com/repository/arangodb-arangodb.md) (14,091 ⭐) — This project is a multi-model database system designed to store and manage information as documents, graphs, and key-value pairs within a single engine. It functions as a graph database and knowledge graph platform, providing the infrastructure to build, query, and visualize structured data models. By integrating vector search capabilities, the system serves as a vector database that supports retrieval-augmented generation for artificial intelligence applications.

The platform distinguishes itself through a unified query language that allows users to perform document lookups, graph traversals
- [ethen8181/machine-learning](https://awesome-repositories.com/repository/ethen8181-machine-learning.md) (3,445 ⭐) — :earth_americas: machine learning tutorials (mainly in Python3)
- [probablykasper/time-machine-inspector](https://awesome-repositories.com/repository/probablykasper-time-machine-inspector.md) (196 ⭐) — Time Machine backup size inspector app
- [donnemartin/data-science-ipython-notebooks](https://awesome-repositories.com/repository/donnemartin-data-science-ipython-notebooks.md) (29,166 ⭐) — This project is a collection of interactive Python notebooks and educational resources designed for mastering data science, machine learning, and numerical computing. It provides a series of practical guides and tutorials covering deep learning, big data processing, and statistical analysis.

The repository features specialized instructional suites for implementing classical machine learning algorithms, building deep learning model architectures, and managing AWS cloud infrastructure. It includes dedicated notebooks for data visualization and numerical computing exercises.

The project covers
- [allegroai/clearml](https://awesome-repositories.com/repository/allegroai-clearml.md) (6,733 ⭐) — ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an experiment tracking tool, a data versioning system, and a pipeline orchestrator, while providing infrastructure for GPU cluster management and model serving.

The platform is distinguished by its ability to handle hybrid-cloud compute scheduling and fractional GPU allocation, allowing multiple workloads to share a single hardware accelerator. It employs a metadata-based approach to data versioning, using virtual views to track large datasets and artifacts without duplicating r
- [clearml/clearml](https://awesome-repositories.com/repository/clearml-clearml.md) (6,740 ⭐) — ClearML is a comprehensive MLOps platform designed to manage the end-to-end machine learning lifecycle, from initial experimentation to production deployment. It provides a suite of integrated tools including a pipeline orchestrator for automating workflows, an experiment tracking tool for logging hyperparameters and metrics, and a metadata-driven data versioning system for managing large-scale datasets and model artifacts.

The platform is distinguished by its advanced compute management and serving capabilities. It features a GPU compute manager that supports fractional resource slicing and
- [dformoso/machine-learning-mindmap](https://awesome-repositories.com/repository/dformoso-machine-learning-mindmap.md) (6,254 ⭐) — A Mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
- [elastic/elasticsearch](https://awesome-repositories.com/repository/elastic-elasticsearch.md) (77,012 ⭐) — Elasticsearch is a distributed search engine and document store designed for the high-performance indexing and retrieval of massive volumes of unstructured data. It functions as a centralized analytics platform, providing a schema-flexible architecture that organizes information into searchable indices while maintaining global cluster state through a distributed consensus mechanism.

The platform distinguishes itself through its integrated approach to observability, security, and advanced analytics. It combines full-text, vector, and hybrid search capabilities with machine learning-driven insi
- [machinelearningmindset/machine-learning-course](https://awesome-repositories.com/repository/machinelearningmindset-machine-learning-course.md) (7,043 ⭐) — ################################################### A Machine Learning Course with Python ###################################################
- [home-assistant/core](https://awesome-repositories.com/repository/home-assistant-core.md) (87,753 ⭐) — Home Assistant is a centralized home automation platform designed to orchestrate diverse internet-connected devices and services. It functions as a local-first control system that normalizes heterogeneous hardware protocols into a unified set of entities, attributes, and services. The core architecture relies on an event-driven state bus and a modular integration model, allowing the system to manage state changes and communicate across decoupled components through standardized interfaces.

The platform distinguishes itself through a highly flexible, declarative configuration framework that all
- [h2oai/h2ogpt](https://awesome-repositories.com/repository/h2oai-h2ogpt.md) (12,016 ⭐) — h2oGPT is a self-hosted platform designed for running large language models and executing retrieval-augmented generation workflows locally. It provides a comprehensive web interface that allows users to index private document collections into searchable databases, enabling context-aware question answering and summarization without exposing sensitive data to external services.

The platform distinguishes itself by offering a modular architecture that supports both local model execution and connections to external inference servers. It facilitates the development of autonomous agents capable of
- [instillai/machine-learning-course](https://awesome-repositories.com/repository/instillai-machine-learning-course.md) (7,043 ⭐) — ################################################### A Machine Learning Course with Python ###################################################