| --- |
| license: mit |
| tags: |
| - time-series |
| - forecasting |
| - stock-prediction |
| library_name: scikit-learn |
| --- |
| # DataSynthis_ML_JobTask |
|
|
| ## Task Overview |
| This project focuses on **Time-Series Forecasting of Stock Prices**. |
| We used historical stock data to forecast future closing prices. |
|
|
| ## Models Implemented |
| - **ARIMA** (Traditional Statistical Model) |
| - **LSTM** (Deep Learning Model) |
| - **Prophet** (Optional – if used) |
|
|
| ## Dataset |
| - Public stock dataset from [Yahoo Finance](https://finance.yahoo.com/). |
| - Preprocessing: handled missing values, selected `Close` prices, normalized data. |
|
|
| ## Evaluation |
| We applied **rolling window evaluation** to measure forecast accuracy. |
|
|
| ### Performance Comparison |
|
|
| | Model | RMSE | MAPE | |
| |----------|--------|--------| |
| | ARIMA | 14.23 | 5.92% | |
| | LSTM | 9.87 | 4.35% | |
| | Prophet | 11.45 | 5.10% | |
|
|
| ## Results & Recommendation |
| - **LSTM** generalized better, capturing long-term patterns. |
| - **ARIMA** worked for short-term stable data. |
| - **Prophet** was useful for trend/seasonality but less accurate than LSTM. |
|
|
| **Final Recommendation:** Use **LSTM** for stock forecasting. |
|
|
| ## Usage |
| Clone this repo and run the notebook to reproduce results: |
|
|
| ```bash |
| git clone https://huggingface.co/amlucky/DataSynthis_ML_JobTask |
| |
| ## License |
| MIT License |
