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

Autoregression

Let's move on to the review of the autoregressive model:

The formula is similar to the linear regression formula, which is where the name comes from. Instead of the coefficient - the past value of x is used.

With statsmodels we can run an autoregressive model AutoReg():

If you notice, the predictions made by the autoregressive model are more accurate than those of the simple moving average.

Let's learn how to evaluate the received results of the trained models. The error is calculated using the mean-squared error. This is done simply with the help of functions sqrt() and mean_squared_error():

Test MSE: 2.072

In the same way, we calculate the error value for the previous model:

Test MSE: 5.321

The smaller the MSE value, the correspondingly smaller the error.

Task

Create an autoregressive model and train it on the dataset shampoo.csv.

Create an autoregression model (Autoreg) with 6 lags for the "Sales" column of the df DataFrame.
Fit the model to data.
Make predictions using the model. Start forecasting at the first row (the start parameter), and set the dynamic parameter to False.
Visualize the results: show the first 150 observations of the "Sales" column of the df DataFrame within the first call of the .plot() function and the first 150 predicted values within the second call.

Everything was clear?

Section 4. Chapter 3

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

Autoregression

Let's move on to the review of the autoregressive model:

The formula is similar to the linear regression formula, which is where the name comes from. Instead of the coefficient - the past value of x is used.

With statsmodels we can run an autoregressive model AutoReg():

If you notice, the predictions made by the autoregressive model are more accurate than those of the simple moving average.

Test MSE: 2.072

In the same way, we calculate the error value for the previous model:

Test MSE: 5.321

The smaller the MSE value, the correspondingly smaller the error.

Task

Create an autoregressive model and train it on the dataset shampoo.csv.

Create an autoregression model (Autoreg) with 6 lags for the "Sales" column of the df DataFrame.
Fit the model to data.
Make predictions using the model. Start forecasting at the first row (the start parameter), and set the dynamic parameter to False.
Visualize the results: show the first 150 observations of the "Sales" column of the df DataFrame within the first call of the .plot() function and the first 150 predicted values within the second call.

Everything was clear?

Section 4. Chapter 3