Time Series Analysis using Deep learning

Time series data is everywhere, from financial markets and weather conditions to stock trading and server logs. My analysis explores a complete pipeline of time series analysis and forecasting using the three different ways:

Statistical Modeling

Machine Learning

Deep Learning

The project aims to give a modular, extensible, and practical foundation for time series projects in both academia and industry. This repository contains implementations of deep learning models for time series analysis, including LSTM, GRU, and Transformer models. It focuses on forecasting tasks such as stock price prediction.

Table of Contents

• Introduction
• Statistical Models
• Machine Learning Models
• Deep Learning Models
• Tools & Libraries
• Results & Visualizations
• Future Work

Features

• Data preprocessing for time series data.
• Statistical and Deep learning models (LSTM, GRU, Transformers) for forecasting.
• Visualization tools for model performance.

Statistical Models

• ARIMA (AutoRegressive Integrated Moving Average)

• SARIMA (Seasonal ARIMA)

• GARCH (Generalized Autoregressive Conditional Heteroskedasticity)

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Machine Learning Models

• Random Forest
• XGBoost
• Support Vector Machine

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Deep Learning Models

• LSTM (Long Short-Term Memory)
• GRU & Bidirectional-LSTM