Time Series Analysis

Time Series Analysis

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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

Store Demand Forecast

As in the previous problem, slightly different models are currently used to forecast demand, which is more complicated than the usual ARIMA. Which, for example? SARIMAX.

This model is very similar to the ARIMA model, except that there is an additional set of autoregressive and moving average components.

The SARIMA model allows data to be distinguished by seasonal frequency as well as other non-seasonal differences. Knowing which options are the best can be made easier with automatic option search frameworks like pmdarina.

You can use SARIMA with statsmodels:

The moving average can also be used to predict demand. However, the results we can get using this method can surpass even XGBoost (reduces the error by 32%). But what should we expect from such a simple method?

In any case, your main task in time series prediction is the optimal choice of the model size (its computational performance) and the results it can bring.

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Section 6. Chapter 2

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