Datasets:
ArXiv:
License:
| { | |
| "name": "29_Financial_Time_Series_Prediction_LSTM_ML", | |
| "query": "Could you help me set up a financial time series prediction system using an LSTM model with some real-world Financial Analysis, like stock prices or Bitcoin prices? First, we'll need to clean the data, taking care of any missing values and outliers in `src/data_loader.py`. Then, let's convert the time series data into a supervised learning format using a time window in `src/data_loader.py`. Finally apply a LSTM model for prediction, where the LSTM model is implemented in `src/model.py`. Once you've got the predictions, save the results as `results/prediction_results.text`. Create an interactive dashboard visualizing prediction results using Dash and save the implementation in `src/dashboard.py`. Finally, I'd appreciate a Markdown document that shows the model architecture, training process, and performance analysis, saved as `results/report.md`. Make sure the system manages the start and stop of the Dash app automatically to save resources. Thanks so much!", | |
| "tags": [ | |
| "Financial Analysis", | |
| "Supervised Learning", | |
| "Time Series Forecasting" | |
| ], | |
| "requirements": [ | |
| { | |
| "requirement_id": 0, | |
| "prerequisites": [], | |
| "criteria": "Some real-world financial time series data (e.g., \"stock prices\" or \"Bitcoin prices\") is loaded in `src/data_loader.py`.", | |
| "category": "Dataset or Environment", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 1, | |
| "prerequisites": [ | |
| 0 | |
| ], | |
| "criteria": "Data cleaning is performed, including handling missing values and outliers in `src/data_loader.py`.", | |
| "category": "Data preprocessing and postprocessing", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 2, | |
| "prerequisites": [ | |
| 1 | |
| ], | |
| "criteria": "A time window is used to convert the time series data into a supervised learning problem. Please implement this in `src/data_loader.py`.", | |
| "category": "Data preprocessing and postprocessing", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 3, | |
| "prerequisites": [], | |
| "criteria": "An \"LSTM\" model is used for financial time series prediction and implemented in `src/model.py`.", | |
| "category": "Machine Learning Method", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 4, | |
| "prerequisites": [ | |
| 2, | |
| 3 | |
| ], | |
| "criteria": "Prediction results saved as `results/prediction_results.txt`.", | |
| "category": "Other", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 5, | |
| "prerequisites": [ | |
| 2, | |
| 3 | |
| ], | |
| "criteria": "An interactive visualization dashboard of prediction results is created using \"Dash\". The implementation is saved in `src/visualize.py`.", | |
| "category": "Visualization", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 6, | |
| "prerequisites": [ | |
| 2, | |
| 3, | |
| 4, | |
| 5 | |
| ], | |
| "criteria": "A Markdown document containing the model architecture, training process, and performance analysis is generated, and saved as `results/report.md`.", | |
| "category": "Other", | |
| "satisfied": null | |
| } | |
| ], | |
| "preferences": [ | |
| { | |
| "preference_id": 0, | |
| "criteria": "The \"Dash\" dashboard should allow users to interact with the prediction results, enabling exploration of different time frames and zooming into specific periods for detailed analysis.", | |
| "satisfied": null | |
| }, | |
| { | |
| "preference_id": 1, | |
| "criteria": "During development, the system should automatically manage the start and stop of the \"Dash\" application to prevent unnecessary resource usage.", | |
| "satisfied": null | |
| } | |
| ], | |
| "is_kaggle_api_needed": false, | |
| "is_training_needed": true, | |
| "is_web_navigation_needed": false | |
| } |