README.md

metadata

title: DataSynthis ML Job Task
emoji: 📈
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit

DataSynthis_ML_JobTask – Gradio Space

Models

• ARIMA → classical statistical model.
• Prophet → trend + seasonality decomposition.
• LSTM (Multivariate) → deep learning with multiple features.

Data: AAPL daily prices (2015–2025)

How it works

• Forecast tab: Choose horizon (7–90d). See future projections overlayed on history.
• Backtest tab: Evaluate models on the last N days (RMSE & MAPE table).

Files

• data/AAPL_stock_2015_2025.csv
• models/arima_model.pkl, models/prophet_model.pkl, models/lstm_model.h5, models/lstm_scaler.pkl
• app.py, requirements.txt

LSTM multi-step uses naive future covariates for demo. For production, predict covariates or use a univariate target model.