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.
• 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:

git clone https://huggingface.co/amlucky/DataSynthis_ML_JobTask

##  License
MIT License