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

Challenge

Завдання

Create an autoregressive model to predict the dataset aapl.csv. After, print the results and the model error.

Read the aapl.csv dataset.
Create an autoregressive model (AutoReg) with 3 lags for the X data and assign it to the model variable.
Fit model to the data and assign it to the model_fit variable.
Predict the first 30 values.
Visualize the results: display the first 30 values of X within the first call of the print() function, and first 30 values of the predictions within the second call.
Calculate the RMSE (square root of the mean squared error) and display it.

Все було зрозуміло?

Секція 4. Розділ 5

Зміст курсу

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

Challenge

Завдання

Create an autoregressive model to predict the dataset aapl.csv. After, print the results and the model error.

Read the aapl.csv dataset.
Create an autoregressive model (AutoReg) with 3 lags for the X data and assign it to the model variable.
Fit model to the data and assign it to the model_fit variable.
Predict the first 30 values.
Visualize the results: display the first 30 values of X within the first call of the print() function, and first 30 values of the predictions within the second call.
Calculate the RMSE (square root of the mean squared error) and display it.

Все було зрозуміло?

Секція 4. Розділ 5