Time Series Analysis

1. Time Series: Let's Start

Start from the introduction to time series to move on to more in-depth topics. Find out what it is, how often time series occur in real business, how to analyze them and make forecasts.

Introduction

Practical Examples

Model Types

Implementation in Python

Visualization

2. Time Series Processing

What is the difference between trend and seasonality? Is the data I'm working with stationary or not? In this section, you will learn how to analyze characteristics of time series using Python!

Reading and Visualizing Data

Stationarity

Autocorrelation

Decomposition

3. Time Series Visualization

Learn more about time series through… their visualization! Explore your data with 2D and 3D graphs.

Simple Time Series

Customize Your Plot

Challenge

4. Stationary Models

What will be our first step? Let's start with stationary models! Create your first predictive model that will forecast time series.

Examples of Stationary Time Series

Simple Moving Average

Autoregression

Autoregressive-Integrated-Moving-Average Model

Challenge

5. Non-Stationary Models

Most of the data you work with is non-stationary. Learn how to process such data and what models to use to predict it.

Examples of Non-Stationary Time Series

Convert Non-Stationary Data to Stationary

Challenge 1

Challenge 2

6. Solve Real Problems

Let's dive into a variety of interesting tasks that you may encounter while working with time series! Find out how big these challenges are and what models we can use to solve them.

Weather Forecasting

Store Demand Forecast

Financial Markets

Predict the Emergence of Repeat Customers

Medicine: EEG Forecasting

Introduction

Time series are the most common data to work with. Their main difference from any other data type is their dependence on time. The data change period can be anything: seconds, minutes, days, etc.

What a common dataset with time series looks like:

As you can see, the main difference between such a dataset and others is the presence of a datetime column responsible for the time indicator. In the example, a sample was written in the dataset exactly every hour.

Time series have many indicators we must analyze: seasonality, cyclicality, trends, stationarity, etc.

Based on these indicators, we can model the behavior of time series and predict their development.

In the following sections, you will get acquainted with time series processing algorithms and statistical models such as: autoregressive model, moving average, etc.

Everything was clear?

Section 1. Chapter 1

Course Content

Time Series Analysis

1. Time Series: Let's Start

Introduction Practical Examples Model Types Implementation in Python Visualization

2. Time Series Processing

Reading and Visualizing Data Stationarity Autocorrelation Decomposition

3. Time Series Visualization

Simple Time Series Customize Your Plot Challenge

4. Stationary Models

Examples of Stationary Time Series Simple Moving Average Autoregression Autoregressive-Integrated-Moving-Average Model Challenge

5. Non-Stationary Models

Examples of Non-Stationary Time Series Convert Non-Stationary Data to Stationary Challenge 1 Challenge 2

6. Solve Real Problems

Weather Forecasting Store Demand Forecast Financial Markets Predict the Emergence of Repeat Customers Medicine: EEG Forecasting

Time Series Analysis

Introduction

Time series are the most common data to work with. Their main difference from any other data type is their dependence on time. The data change period can be anything: seconds, minutes, days, etc.

What a common dataset with time series looks like:

Time series have many indicators we must analyze: seasonality, cyclicality, trends, stationarity, etc.

Based on these indicators, we can model the behavior of time series and predict their development.

In the following sections, you will get acquainted with time series processing algorithms and statistical models such as: autoregressive model, moving average, etc.

Everything was clear?

Section 1. Chapter 1