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Course Curriculum

Introduction
1.1 What does the course cover 00:00:00
Setting up the working environment
2.1 Installing Anaconda 00:00:00
2.2 Jupyter Dashboard – Part 1 00:00:00
2.3 Jupyter Dashboard – Part 2 00:00:00
2.1 Setting up the environment – Do not skip, please 00:00:00
2.5 Why Python and Jupyter 00:00:00
Introduction to Time Series in Python
3.1 Examining the Data 00:00:00
3.2 Introduction to Time Series Data 00:00:00
3.3 Loading the Data 00:00:00
3.4 Notation for Time Series Data 00:00:00
3.5 Peculiarities 00:00:00
Creating a time series object in python
4.1 Adding and Removing Columns in a Data Frame 00:00:00
4.2 Filling Missing Values 00:00:00
4.3 Setting the Frequency 00:00:00
4.4 Splitting up the Data 00:00:00
4.5 Transforming String inputs into DateTime Values 00:00:00
4.6 Using Dates as Indices 00:00:00
Working with time sries in python
5.1 Correlation Between Past and Present Values 00:00:00
5.2 Determining Weak Form Stationarity 00:00:00
5.3 Random Walk 00:00:00
5.4 Seasonality 00:00:00
5.5 Stationarity 00:00:00
5.6 The ACF 00:00:00
5.7 The PACF 00:00:00
5.8 White Noise 00:00:00
Picking the correct model
6.1 A Quick Guide to Picking the Correct Model 00:00:00
The Autoregressive (AR) Model
7.1 Examining the ACF and PACF of Prices 00:00:00
7.2 Examining the ACF and PACF of Returns 00:00:00
7.3 Examining the AR Model Residuals 00:00:00
7.4 Fitting an AR(1) Model for Index Prices 00:00:00
7.5 Fitting Higher Lag AR Models for Prices 00:00:00
7.6 Fitting Higher Lag AR Models for Returns 00:00:00
7.7 Model Selection for Normalized Returns 00:00:00
7.8 Normalizing Values 00:00:00
7.9 Unexpected Shocks from Past Periods 00:00:00
7.10 Using Returns 00:00:00
The moving Average (MA) model
8.1 ARMA for Prices 00:00:00
8.2 ARMA Models and Non-stationary Data 00:00:00
8.3 Examining the ARMA Model Residuals of Returns 00:00:00
8.4 Fitting a Higher-Lag ARMA Model for Returns – part 1 00:00:00
8.5 Fitting a Higher-Lag ARMA Model for Returns – part 2 00:00:00
8.6 Fitting a Higher-Lag ARMA Model for Returns – part 3 00:00:00
8.7 Fitting a Simple ARMA Model for Returns 00:00:00
The Autoregressive Integrated Moving Average (ARIMA) Model
9.1 Fitting a Higher Lag ARIMA Model for Prices – part 1 00:00:00
9.2 Fitting a Higher Lag ARIMA Model for Prices – part 2 00:00:00
9.3 Fitting a Simple ARIMA Model for Prices 00:00:00
9.4 Higher Levels of Integration 00:00:00
9.5 Predicting Stability 00:00:00
9.6 Using ARIMA Models for Returns 00:00:00
The ARCH Model
10.1 A More Detailed Look of the ARCH Model 00:00:00
10.2 An ARMA Equivalent of the ARCH Model 00:00:00
10.3 Higher Lag ARCH Models 00:00:00
10.4 The arch_model Method 00:00:00
10.5 The Simple ARCH Model 00:00:00
The GARCH Model
11.1 Higher-Lag GARCH Models 00:00:00
11.2 The ARMA and the GARCH 00:00:00
11.3 The Goal Behind Modeling 00:00:00
11.4 The Simple GARCH Model 00:00:00

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