Time Series Analysis

Time Series Analysis

0%

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.

Practical Examples

Implementation in Python

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

Autocorrelation

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

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

Autoregressive-Integrated-Moving-Average Model

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

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

Course Content

Time Series Analysis

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

Medicine: EEG Forecasting

And the last task that we will consider is the prediction of the EEG signal. Time series in medicine are very important data, the analysis of which can solve many problems. One of them is the analysis of the EEG signal for the prediction of epileptic seizures. The example we'll look at uses the ARIMA-GARCH predictive model.

We will not delve into the operation of this model in detail but only mention that the GARCH model can capture the flat period and fluctuation period of time series.

Processing medical temporal data is no different from a normal data processing algorithm: you still need to remove noise, fill in missing data, and turn non-stationary data into stationary data.

This small section was devoted to a brief overview of various real-world problems for which, as you understand, models such as moving average, autoregressive model, and ARIMA are just a starter. In the future, these models will be modified depending on the type of problem, and sometimes they are not at all effective enough to solve certain problems.

Everything was clear?

Thanks for your feedback!

Section 6. Chapter 5

some-alt