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Stock Price Forecasting & Analysis with Statistical and Deep Learning Models
Time series forecasting pipeline combining statistical models (Holt-Winters, SARIMA) and LSTM neural networks to analyze and predict stock price trends.
Overview
This project implements a comprehensive time series forecasting pipeline for stock price data, combining statistical and deep learning models. It addresses the challenge of predicting financial trends by automating data analysis, visualization, and model comparison.
Features
- Data cleaning and preprocessing for time series consistency
- Exploratory data analysis with statistical summaries and visualizations
- Candlestick chart visualization for financial data
- Forecasting using Holt-Winters (Exponential Smoothing)
- Advanced forecasting with SARIMA models
- Deep learning-based prediction using LSTM networks
- Model comparison using performance metrics and trend analysis
- Export of cleaned data and forecasts to CSV for reporting
- Creation of a Dashboard and Report in Power BI
Tech Stack
- Python
- Pandas
- Matplotlib / mplfinance
- Statsmodels (Holt-Winters, SARIMA)
- TensorFlow / Keras (LSTM)
Power BI Dashboard
- Dashboard to visualize and compare the model predictions for better forecasting and analysis.
