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---
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 |