8. Time Series Analysis and Forecasting in Machine Learning with Andrey Holz, Ph.D.

ML Lectures Playlist: https://youtube.com/playlist?list=PLGWXNgjLi7BTp_T4HU-KkbHBerAE8gRp4&si=Jc00z8S92vhNuzlN Join Dr. Andrey Holz in this in-depth lecture on **Time Series Analysis and Forecasting** in Machine Learning, designed to provide a thorough understanding of fundamental concepts, advanced modeling approaches, and practical techniques. 📚 **Lecture Content:** - **Introduction to Time Series Data:** - Definition and unique characteristics of time series data. - Key components: trend, seasonality, cyclicity, and irregularity (noise). - **Mathematical Foundations:** - Importance of stationarity, autocorrelation, and partial autocorrelation. - Statistical tests such as ADF and KPSS for stationarity checks. - **Modeling Approaches:** - Classical models: ARIMA and SARIMA for linear patterns and seasonality. - Modern Machine Learning models: Random Forests, XGBoost, and Prophet for non-linear relationships and complex seasonal patterns. - **Data Preparation and Feature Engineering:** - Handling missing values and performing differencing for stationarity. - Creating lag features, rolling statistics, and encoding time-based attributes. - **Evaluation Metrics:** - Comprehensive review of metrics such as RMSE, MAE, MAPE, and residual diagnostics. - **Advanced Topics:** - Methods for anomaly and changepoint detection. - Techniques for multivariate time series analysis. - **Best Practices:** - Dataset splitting strategies specific to time series, including rolling and sliding windows. - Estimating confidence intervals and incorporating domain knowledge. 💻 **Practical Demonstration:** Watch step-by-step implementation in Python, demonstrating how to preprocess data, build models, and evaluate their performance on real-world datasets. ✏️ **Timestamps:** - 0:00:00 Introduction and Agenda - 0:02:16 What is a Time Series: Definition, Components, Component calculation - 0:05:41 Time Series VS Regression - 0:06:53 Stationarity in Time Series: Definition, importance, tests. - 0:10:57 Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) - 0:15:00 CODING: Time Series Decomposition - 0:20:49 TimeSeries Modeling: ARIMA/SARIMA - 0:25:00 TimeSeries Modeling: Classical Machine Learning Approach - 0:26:32 TimeSeries Modeling: Prophet model overview, features. - 0:29:52 TimeSeries Modeling: Hybrid Models and other Methods - 0:33:14 CODING: TimeSeries Modeling - 0:42:59 Data Preprocessing amd Feature Engineering tips fpr Time Series - 0:48:42 Data Splitting for Time Series - 0:51:43 Metrics for Time Series - 0:52:55 Confidence Intervals for Time Series - 0:58:18 Advanced Topics Overview: Advanced Seasonality and Trends - 0:59:20 Advanced Topics Overview: Multivariate Time Series - 1:00:27 Advanced Topics Overview: Time Series Anomaly Detection - 1:01:42 Advanced Topics Overview: Time Series Changepoint Detection - 1:02:45 Best Practices Summary - 1:06:01 Key Takeaways - 1:07:56 Thank You + some motivation 🌟 **Key Takeaways:** - Deep understanding of time series data and its components. - Comparative insights into classical and modern modeling approaches. - Importance of data preparation, validation, and evaluation for accurate forecasting. 👍 Subscribe for more lectures and tutorials by Dr. Holz on advanced topics in machine learning and data science. #TimeSeriesAnalysis #Forecasting #MachineLearning #ARIMA #SARIMA #Prophet #Python #MLModels #DrHolzLectures