Time Series Analysis

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

Implementation in Python

Having become familiar with the models that will allow us to predict time series, you probably have a question, which Python libraries will be used?

Firstly, in order to better understand the mathematical mechanism - you can implement one of the models yourself in Python.

While we will load the rest of the models through libraries such as statsmodels:

python

python

We have formed a prediction for the next hundred months in the plot above.

The above code uses a model that captures the last "pattern" of seasonality, i.e., the same repeating segment.

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 4

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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

Implementation in Python

Having become familiar with the models that will allow us to predict time series, you probably have a question, which Python libraries will be used?

Firstly, in order to better understand the mathematical mechanism - you can implement one of the models yourself in Python.

While we will load the rest of the models through libraries such as statsmodels:

python

python

We have formed a prediction for the next hundred months in the plot above.

The above code uses a model that captures the last "pattern" of seasonality, i.e., the same repeating segment.

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 4

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