{"id":26301939,"url":"https://github.com/abu14/climate-temperature-forecasting-lstm","last_synced_at":"2025-03-15T07:17:34.532Z","repository":{"id":279299723,"uuid":"938355160","full_name":"abu14/Climate-Temperature-Forecasting-LSTM","owner":"abu14","description":"A climate forecasting model using the Jena Climate dataset, leveraging LSTM networks to predict temperature from 14 atmospheric variables recorded every 10 minutes.","archived":false,"fork":false,"pushed_at":"2025-03-12T12:09:24.000Z","size":14654,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-12T13:22:52.221Z","etag":null,"topics":["eda","lstm-neural-networks","rnn-tensorflow","tensorflow","time-series"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/abu14.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}},"created_at":"2025-02-24T20:29:43.000Z","updated_at":"2025-03-12T12:16:23.000Z","dependencies_parsed_at":"2025-02-24T21:44:23.803Z","dependency_job_id":null,"html_url":"https://github.com/abu14/Climate-Temperature-Forecasting-LSTM","commit_stats":null,"previous_names":["abu14/climate-temperature-forecasting-lstm"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abu14%2FClimate-Temperature-Forecasting-LSTM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abu14%2FClimate-Temperature-Forecasting-LSTM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abu14%2FClimate-Temperature-Forecasting-LSTM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abu14%2FClimate-Temperature-Forecasting-LSTM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/abu14","download_url":"https://codeload.github.com/abu14/Climate-Temperature-Forecasting-LSTM/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243695546,"owners_count":20332629,"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":["eda","lstm-neural-networks","rnn-tensorflow","tensorflow","time-series"],"created_at":"2025-03-15T07:17:33.921Z","updated_at":"2025-03-15T07:17:34.525Z","avatar_url":"https://github.com/abu14.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## 🌡️ **Time Series Forecasting for Climate Data using LSTM**\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)\n[![TensorFlow 2.12+](https://img.shields.io/badge/TensorFlow-2.12+-FF6F00?logo=tensorflow)](https://www.tensorflow.org/)\n\nThis project focuses on climate forecasting using the Jena Climate dataset, which includes 14 atmospheric variables recorded every 10 minutes from January 1, 2009, to December 31, 2016. Leveraging Long Short-Term Memory (LSTM) networks, the model aims to predict temperature based on historical data.\n\n#### **Key features include:**\n* Data cleaning and feature engineering to enhance model performance.\n* Utilization of LSTM for time-series forecasting.\n* Achieved a loss of 0.7845 on the training set and 0.0653 on the validation set, indicating strong predictive capability.\n* The analysis provides insights into the relationships between different climatic variables, offering valuable information for climate-related studies.\n\n\u003e Refer to the notebook [Here](https://github.com/abu14/Climate-Temperature-Forecasting-LSTM/blob/main/notebooks/Time_Series_Climate_Forecasting_using_LSTM.ipynb) for more detail.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"assets/prediction_performance.png\" alt=\"Digit Recognition\"\u003e\n  \n\u003c/p\u003e\n\n\n### 🔧 **Tools Used**\n\n\u003cp\u003e\n\u003cimg src=\"https://img.shields.io/badge/-Python-3776AB?style=flat\u0026logo=python\u0026logoColor=white\"\u003e\n\u003cimg src=\"https://img.shields.io/badge/-TensorFlow-FF6F00?style=flat\u0026logo=tensorflow\u0026logoColor=white\"\u003e  \n\u003cimg src=\"https://img.shields.io/badge/-Keras-D00000?style=flat\u0026logo=keras\u0026logoColor=white\"\u003e \n\u003cimg src=\"https://img.shields.io/badge/-scikit--learn-F7931E?style=flat\u0026logo=scikit-learn\u0026logoColor=white\"\u003e\n\u003cimg src=\"https://img.shields.io/badge/-NumPy-013243?style=flat\u0026logo=numpy\u0026logoColor=white\"\u003e\n\u003cimg src=\"https://img.shields.io/badge/-Pandas-150458?style=flat\u0026logo=pandas\u0026logoColor=white\"\u003e\n\u003cimg src=\"https://img.shields.io/badge/-Matplotlib-11557C?style=flat\u0026logo=matplotlib\u0026logoColor=white\"\u003e\n\u003cimg src=\"https://img.shields.io/badge/-Seaborn-3888E3?style=flat\u0026logo=seaborn\u0026logoColor=white\"\u003e\n\u003c/p\u003e\n\n\n\n### 📦 **Installation**\n\n#### Prerequisites\n* numpy\n* pandas\n* seaborn\n* matplotlib\n* plotly\n* scikit-learn\n* tensorflow\n\n\n\n\n## 📂 Project Structure\n```\nproject-root/\n├── data/             \n├── models/             \n├── notebooks/\n├── src/                \n│   ├── data_processing.py\n│   ├── features.py\n│   ├── modeling.py\n│   └── visualize.py\n└── scripts/           \n```\n\n\n\n## 🧠 Model Architecture\n\n```python\nSequential(\n    LSTM(32, return_sequences=True, input_shape=(look_back, n_features)),\n    Dropout(0.2),\n    ReLU(),\n    LSTM(32, return_sequences=False),\n    Dropout(0.2),\n    Dense(1)\n)\n```\n\n\n\n## 📄 License\nDistributed under the MIT License. See LICENSE for more information.\n\n## 🙏 Acknowledgments\nJena Climate Dataset provided by Max Planck Institute\n\n\n\u003c!-- CONTACT --\u003e\n## **Contact**\n\n##### Abenezer Tesfaye\n\n⭐️ Email - tesfayeabenezer64@gmail.com\n \nProject Link: [Github Repo](https://github.com/abu14/Climate-Temperature-Forecasting-LSTM)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabu14%2Fclimate-temperature-forecasting-lstm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabu14%2Fclimate-temperature-forecasting-lstm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabu14%2Fclimate-temperature-forecasting-lstm/lists"}