Time Series Class - Part 1 - Dr. Ioannis Papastathopoulos, University of Edinburgh

Dive into a comprehensive lecture on time series analysis with Dr. Ioannis Papastathopoulos from the University of Edinburgh. Explore fundamental concepts such as moving average, autoregressive, and ARMA models, as well as advanced topics like state-space models and recurrent neural networks. Learn about parameter estimation, likelihood-based inference, and forecasting techniques. Gain insights into time series basics, measures of dependence, stationarity, white noise, random walk, and empirical covariance. Understand the effects of autocorrelation, statistical tests for stationarity, and methods for removing trends. Delve into causal processes and normal processes, providing a solid foundation for time series analysis in various applications.

Introduction
Motivation
Time series basics
Measures of dependence
Stationarity
Secondorder stationarity
White noise
Random walk
Empirical Covariance
Effect of autocorrelation
Statistical tests
Stationarity tests
Removing trends
Causal processes
Normal processes