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{
"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
} |