Advanced Time Series Machine Learning Tutorials

Welcome to the Advanced Time Series Machine Learning section! 📊🧠 This guide is designed for learners who want to dive deeper into forecasting, anomaly detection, and modeling complex temporal patterns.

Key Concepts

• Time Series Forecasting 📈: Predict future values based on historical data using models like ARIMA, LSTM, or Prophet.
• Feature Engineering 🛠️: Extract meaningful patterns from raw time series data (e.g., rolling averages, lag features).
• Model Evaluation 🧪: Use metrics like MAE, RMSE, or MAPE to assess performance.
• Deep Learning for Time Series 🤖: Explore CNNs, RNNs, and Transformers for capturing long-term dependencies.

Practice Tutorials

1. LSTM for Stock Price Prediction

   • Watch Tutorial 🎥
   • 

     LSTM_Network

2. Prophet: Seasonality and Trends

   • Watch Tutorial 🎥
   • 

     Time_Series_Forecasting

3. Transformers for Temporal Data

   • Watch Tutorial 🎥
   • 

     Transformer_Model

Expand Your Knowledge

For a beginner-friendly introduction to time series analysis, check out:
Time Series Basics Tutorial

📌 Pro Tip: Use this tool to visualize your data and experiment with different models!

Advanced_Time_Series