{"id":29022979,"url":"https://github.com/atumat/time-series-analysis","last_synced_at":"2026-05-04T20:39:07.017Z","repository":{"id":298525176,"uuid":"999400396","full_name":"atumat/time-series-analysis","owner":"atumat","description":"The project visualises trends in stock data and explores various time-series analysis models such as ARIMA, GARCH and Deep learning techniques like LSTM, GRU, and Transformer based models to predict stock prices","archived":false,"fork":false,"pushed_at":"2025-06-11T14:21:39.000Z","size":26,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-06-11T16:02:27.135Z","etag":null,"topics":["data-science","gru","keras","python","rnn-lstm","tensorflow"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/atumat.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-06-10T07:38:10.000Z","updated_at":"2025-06-11T14:21:43.000Z","dependencies_parsed_at":"2025-06-11T16:02:42.989Z","dependency_job_id":"df188f4c-21c2-4daf-b99e-288b22c2ab71","html_url":"https://github.com/atumat/time-series-analysis","commit_stats":null,"previous_names":["atumat/time-series-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/atumat/time-series-analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atumat%2Ftime-series-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atumat%2Ftime-series-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atumat%2Ftime-series-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atumat%2Ftime-series-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/atumat","download_url":"https://codeload.github.com/atumat/time-series-analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atumat%2Ftime-series-analysis/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261990349,"owners_count":23241188,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","gru","keras","python","rnn-lstm","tensorflow"],"created_at":"2025-06-26T03:04:34.675Z","updated_at":"2026-05-04T20:39:07.013Z","avatar_url":"https://github.com/atumat.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1\u003eTime Series Analysis using Deep learning \u003c/h1\u003e\n\u003cp align=\"center\"\u003e \u003cimg src=\"https://img.shields.io/badge/domain-time%20series%20forecasting-blue\" /\u003e \u003cimg src=\"https://img.shields.io/badge/methods-Statistical%20%7C%20ML%20%7C%20DL-green\" /\u003e \u003cimg src=\"https://img.shields.io/badge/status-In progress%20-important\" /\u003e \u003c/p\u003e\n\nTime series data is everywhere, from financial markets and weather conditions to stock trading and server logs. My analysis explores a complete pipeline of time series analysis and forecasting using the three different ways:\n\nStatistical Modeling\n\nMachine Learning\n\n\n\n\nDeep Learning\n\nThe project aims to give a modular, extensible, and practical foundation for time series projects in both academia and industry.\nThis repository contains implementations of deep learning models for time series analysis, including LSTM, GRU, and Transformer models. It focuses on forecasting tasks such as stock price prediction.\n\n##  Table of Contents\n\n  - [Introduction](#-introduction)\n  - [Statistical Models](#-statistical-models)\n  - [Machine Learning Models](#-machine-learning-models)\n  - [Deep Learning Models](#-deep-learning-models)\n  - [Tools \u0026 Libraries](#️-tools--libraries)\n  - [Results \u0026 Visualizations](#-results--visualizations)\n  - [Future Work](#-future-work)\n\n\n\n## Features\n- Data preprocessing for time series data.\n- Statistical and Deep learning models (LSTM, GRU, Transformers) for forecasting.\n- Visualization tools for model performance.\n\n##  Statistical Models\n\n- **ARIMA (AutoRegressive Integrated Moving Average)**\n\n\n\n- **SARIMA (Seasonal ARIMA)**\n\n- **GARCH (Generalized Autoregressive Conditional Heteroskedasticity)**\n\n\n---\n\n##  Machine Learning Models\n\n- **Random Forest**\n- **XGBoost**\n- **Support Vector Machine**\n\n\n\n\n\n---\n\n##  Deep Learning Models\n\n- **LSTM (Long Short-Term Memory)**\n- **GRU \u0026 Bidirectional-LSTM**\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://miro.medium.com/v2/resize:fit:800/format:webp/1*vYpKL1PjVPjLbU7S6xKkYg.gif\" width=\"500\"/\u003e\n\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fatumat%2Ftime-series-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fatumat%2Ftime-series-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fatumat%2Ftime-series-analysis/lists"}