Introduction
  • Introduction
  • Managing Expectations
  • Python for Time Series Analysis
  • The Basics of Time Series Analysis and Forecasting
  • Select a Forecasting Method
  • The Steps of Forecasting - A Guide for Newbies
  • Intro Quiz
  • Python Script to Download
Time Series Analysis Background Knowledge
  • Time Series Fundamentals
  • The Lynx Dataset
  • Time Series Vectors and Lags
  • Recognizing Time Series Characteristics
  • Stationarity
  • Autocorrelation
  • Visualizing Time Series Data
  • Moving Averages and Smoothers
  • TSA Background Quiz
  • Homework Assignment #1: US Inflation Rates
ARIMA for Univariate, Non-Seasonal Data
  • Introduction to ARIMA Models in Python
  • ARIMA Models for Univariate Time Series
  • ARIMA Parameter Selection
  • ARIMA Residuals
  • Manual ARIMA Model Calculation
  • Identify ARIMA Model Parameters: General Rules
  • ARIMA Forecasts
  • Homework Assignment #2: Singapore LFPR
Models for Seasonal Data
  • The Nottem Dataset
  • Seasonal Decomposition
  • The STLDecompose Package
  • Seasonal Adjustment and Forecasting
  • Quantitative Forecasting Methods: An Overview
  • Exponential Smoothing Background
  • Exponential Smoothing Demo
  • What to Do When Numbers Are Not Enough: Qualitative Forecasting Methods
  • Introduction to Prophet by Facebook
  • Modeling and Forecasting Seasonal Data with Prophet
  • Homework Assignment #3: Seasonal Models
Multivariate Time Series Analysis
  • Introduction to Multivariate Time Series Analysis and Dataset Structure
  • Our Multivariate Time Series Dataset (and Script)
  • Checking for Stationarity and Differencing the MTS
  • Vector Autoregressive Models
  • Fitting a VAR Model and Identifying the Lag Order
  • The Granger Causality Test
  • Forecasting with VAR Models
  • Further References
Homework Solutions
  • Homework Solution #1: US Inflation Rates
  • Homework Solution #2: Singapore LFPR
  • Homework Solution #3: Seasonal Models