In Data Science mainly relies on working with two types of data - cross-sectional and time series. This course will help you master the latter by introducing you to ARMA, Seasonal, Integrated, MAX and Volatility models as well as show you how to forecast them into the future.

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create free accountIn this short section, we’ll tell you a bit more of what the course is about, how its structured and what our goal is.

What does the course cover

In this part of the course, we will explain to you how to set up Python 3 and then load up Jupyter. We’ll also show you what the Anaconda Prompt is and how we use it to download and import new modules.

Setting up the environment - Do not skip, please!

Why Python and Jupyter?

Installing Anaconda

Jupyter Dashboard - Part 1

Jupyter Dashboard - Part 2

Installing the Necessary Packages

In this section of the course, we are going to learn what makes a dataset a time series, and discuss what separates it from cross-sectional data. We’ll introduce the appropriate mathematical notation for such data before loading up a dataset and quickly examining it.

Introduction to Time Series Data

Notation for Time Series Data

Peculiarities

Loading the Data

Examining the Data

Plotting the Data

The QQ Plot

In this section of the course, we will go through the pre-processing aspects of working with time series. We’ll see how to interpret string text as dates and set these dates as indices of the data set. We’ll then set a fixed frequency and account for any missing values before splitting up the set for training and testing. In the appendix, we’ll show you how to import data directly from Yahoo Finance, so you can conduct your own analysis after completing the course.

Transforming String inputs into DateTime Values

Using Dates as Indices

Setting the Frequency

Filling Missing Values

Adding and Removing Columns in a Data Frame

Splitting up the Data

In this section of the course, we’ll examine and visualize some important types of time series, like white noise and a random walk. We’ll then discuss important concepts like stationarity, seasonality and autocorrelation, before exploring the ACF and PACF of a S&P 500’s prices.

White Noise

Random Walk

Stationarity

Determining Weak Form Stationarity

Seasonality

Correlation Between Past and Present Values

The ACF

The PACF

In this short section, we’ll discuss the general rules of manual model selection. We will talk about which models we prefer, what we want to avoid and how to decide between models. We’ll talk about the Log-likelihood and information criterion as measurements of preference among similar models.

A Quick Guide to Picking the Correct Model

In this section, we’ll introduce the Autoregressive Model and see how well it models market index prices and returns. We’ll discuss how to use the PACF to determine the appropriate number of lags for the model and explore the concept of normalizing values and its impact on model selection.

The AR Model

Examining the ACF and PACF of Prices

Fitting an AR(1) Model for Index Prices

Fitting Higher Lag AR Models for Prices

Using Returns

Analysing Returns

Normalizing Values

Model Selection for Normalized Returns

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Examining the AR Model Residuals

Unexpected Shocks from Past Periods

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In this section, we’ll introduce the Moving Average model and see how well it describes price returns. We’ll also have a look at how the MA model performs when dealing with non-stationary data and comment on the mathematical arguments for and against using such models for index prices.

The MA Model

Fitting an MA(1) Model for Returns

Fitting Higher-Lag MA Models for Returns

Examining the MA Model Residuals for Returns

Model Selection for Normalized Returns

Fitting an MA(1) Model for Prices

Past Values and Past Errors

In this section, we’ll combine the two models we just examined – the AR and MA – into one: the ARMA. We’ll examine how they synergize and limit the drawbacks each model has on its own. We’ll then talk about the issues that come along with finding the best-fitting ARMA model and see how checking the model residuals can be beneficial in model selection.

The ARMA Model

Fitting a Simple ARMA Model for Returns

Fitting a Higher-Lag ARMA Model for Returns - part 1

Fitting a Higher-Lag ARMA Model for Returns - part 2

Fitting a Higher-Lag ARMA Model for Returns - part 3

Examining the ARMA Model Residuals of Returns

ARMA for Prices

ARMA Models and Non-stationary Data

In this section of the course, we’ll talk about “integration” and integrated models. We’ll explain why and when we use them, as well as when we should avoid them. Here, we’ll also briefly explain the idea of “MAX” models and how to add exogenous variables to any time series model.

The ARIMA Model

Fitting a Simple ARIMA Model for Prices

Fitting a Higher Lag ARIMA Model for Prices - part 1

Fitting a Higher Lag ARIMA Model for Prices - part 2

Higher Levels of Integration

Using ARIMA Models for Returns

Outside Factors and the ARIMAX Model

Predicting Stability

In this section, we’ll talk about the idea of measuring volatility when we’re looking for stability in our investments. We’ll explain the multiple layers of ARCH models and how they differ from the ARMA family of models we just examined. We’ll spend some time discussing the vast functionality of the “arch_model” method and why it’s important to know the default values for many of its arguments.

The ARCH Model

Volatility

A More Detailed Look of the ARCH Model

The arch_model Method

The Simple ARCH Model

Higher Lag ARCH Models

An ARMA Equivalent of the ARCH Model

In this section of the course, we’ll discuss the generalized version of the ARCH model, also known as the GARCH. We’ll explore why this model is more widely used, how it outperforms high-order ARCH models and why it looks so similar to the ARMA. We’ll then empirically test the known fact that the GARCH(1,1) is the best model for measuring the volatility of price returns.

The GARCH Model

The ARMA and the GARCH

The Simple GARCH Model

Higher-Lag GARCH Models

The Goal Behind Modeling

MODULE 4

This course is part of Module 4 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. Module 4 is focused on developing a specialized, industry-relevant skill set, and students are encouraged to complete Modules 1, 2, and 3 before they start this part of the training. Here, you will learn how to perform Credit Risk Modeling for banks, Customer Analytics for retail or other commercial companies, and Time Series Analysis for finance and stock data.

See All ModulesReal-life project and data. Solve them on your own computer as you would in the office.

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