instances/28_Stock_Price_Prediction_LSTM_YahooFinance_ML.json

{
    "name": "28_Stock_Price_Prediction_LSTM_YahooFinance_ML",
    "query": "Could you help me build a stock price prediction system using an LSTM model and the Yahoo Finance dataset? Please clean the data, including handling missing values and outliers, and use a time window to convert the time series data to a supervised learning problem. The LSTM model should be implemented in `src/model.py`, and the dataset loading, cleaning, and conversion should be implemented in `src/data_loader.py`. Save the prediction results to `results/predictions.txt` and generate and save interactive charts of the prediction results in `results/figures/prediction_interactive.html` using Plotly. Create a Jupyter Notebook with model architecture visualization, training process, and prediction results and save it as a PDF report at `results/report.pdf`.",
    "tags": [
        "Financial Analysis",
        "Supervised Learning",
        "Time Series Forecasting"
    ],
    "requirements": [
        {
            "requirement_id": 0,
            "prerequisites": [],
            "criteria": "The \"LSTM\" model is implemented in `src/model.py`.",
            "category": "Machine Learning Method",
            "satisfied": null
        },
        {
            "requirement_id": 1,
            "prerequisites": [],
            "criteria": "The \"Yahoo Finance\" dataset is loaded in `src/data_loader.py`.",
            "category": "Dataset or Environment",
            "satisfied": null
        },
        {
            "requirement_id": 2,
            "prerequisites": [
                1
            ],
            "criteria": "Data cleaning, including handling missing values and outliers, is performed in `src/data_loader.py`.",
            "category": "Data preprocessing and postprocessing",
            "satisfied": null
        },
        {
            "requirement_id": 3,
            "prerequisites": [
                0,
                2
            ],
            "criteria": "A time window is used to convert the time series data to a supervised learning problem. Please save the implementation in `src/data_loader.py`.",
            "category": "Data preprocessing and postprocessing",
            "satisfied": null
        },
        {
            "requirement_id": 4,
            "prerequisites": [
                2,
                3
            ],
            "criteria": "Prediction results are saved in `results/predictions.txt`.",
            "category": "Other",
            "satisfied": null
        },
        {
            "requirement_id": 5,
            "prerequisites": [
                0,
                1,
                2
            ],
            "criteria": "Interactive charts of prediction results are generated using \"Plotly\" and saved in `results/figures/prediction_interactive.html`.",
            "category": "Visualization",
            "satisfied": null
        },
        {
            "requirement_id": 6,
            "prerequisites": [
                0,
                1,
                2,
                3,
                4
            ],
            "criteria": "A Jupyter Notebook containing the model architecture visualization, training process, and prediction results are created and saved as PDF report as `results/report.pdf`.",
            "category": "Other",
            "satisfied": null
        }
    ],
    "preferences": [],
    "is_kaggle_api_needed": false,
    "is_training_needed": true,
    "is_web_navigation_needed": false
}