{"id":14958781,"url":"https://github.com/tgjeon/tensorflow-tutorials-for-time-series","last_synced_at":"2025-04-12T15:43:25.804Z","repository":{"id":90561529,"uuid":"59971429","full_name":"tgjeon/TensorFlow-Tutorials-for-Time-Series","owner":"tgjeon","description":"TensorFlow Tutorial for Time Series Prediction","archived":false,"fork":false,"pushed_at":"2017-08-01T07:55:55.000Z","size":2369,"stargazers_count":1104,"open_issues_count":11,"forks_count":417,"subscribers_count":89,"default_branch":"master","last_synced_at":"2025-04-03T16:13:18.072Z","etag":null,"topics":["rnn","series-prediction","tensorflow-tutorials"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# TensorFlow Tutorial for Time Series Prediction\n\nThis tutorial is designed to easily learn TensorFlow for time series prediction. \nEach tutorial subject includes both code and notebook with descriptions.\n\n## Tutorial Index\n\n#### MNIST classification using Recurrent Neural Networks (RNN)\n- Classification for MNIST using RNN ([notebook](https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series/blob/master/mnist-rnn.ipynb))\n\n#### Time series prediction using Recurrent Neural Networks (RNN)\n\n- Prediction for sine wave function using Gaussian process ([code](https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series/blob/master/gp-for-sine-wave.py) / [notebook](https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series/blob/master/gp-for-sine-wave.ipynb))\n- Prediction for sine wave function using RNN ([code](https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series/blob/master/lstm-for-sine-wave.py) / [notebook](https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series/blob/master/lstm-for-sine-wave.ipynb))\n- Prediction for electricity price ([code](https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series/blob/master/lstm-for-epf.py) / notebook)\n\nThese codes are adapted from the source: https://github.com/mouradmourafiq/tensorflow-lstm-regression\n\n## Slide materials\n- [Slides on slideshare (TensorFlow-KR Meetup)](http://www.slideshare.net/TaegyunJeon1/electricity-price-forecasting-with-recurrent-neural-networks)\n- [Slides on github (KSC 2016 Tutorial)](https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series/raw/master/KSC2016%20-%20Recurrent%20Neural%20Networks.pptx)\n\n#### Dependencies\n\n```\nPython (3.4.4)\nTensorFlow (r0.9)\nnumpy (1.11.1)\npandas (0.16.2)\ncuda (to run examples on GPU)\n```\n\n#### Dataset\n\n- Energy Price Forecast 2016: http://complatt.smartwatt.net\n- Or use the uploaded csv file for price history for 2015.\n\n#### Current issues\n\n- ```tf:split_squeeze``` is deprecated and will be removed after 2016-08-01. Use ```tf.unpack``` instead.\n- ```tf:dnn``` is deprecated and will be removed after 2016-08-01. Use ```tf.contrib.layers.stack``` instead.\n\nNow I am working on modifying previous source code for tensorflow ver. 0.10.0rc0.\n\n#### Notice\n- I have received many request for revising the code for the current tensorflow version.\n- I will provide summarized presentation file for the theory of time series prediction.\n- And How to apply the tensorflow implementation for kaggle competitions.\n- Target implementation will be tensorflow v1.2\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftgjeon%2Ftensorflow-tutorials-for-time-series","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftgjeon%2Ftensorflow-tutorials-for-time-series","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftgjeon%2Ftensorflow-tutorials-for-time-series/lists"}