Master Time Series Analysis and Forecasting with Python 2025 from Udemy

Updates December 2024:

Amazon AutoGluon launched
Library requirements.txt file for all sections added

Updates October 2024:

Amazon Chronos launched
N-BEATS launched

Updates September 2024:

TFT and TFT Capstone Project added

Updates August 2024:

Course remade 100%
Silverkite, LSTM and Projects added

Welcome to the most exciting online course about Forecasting Models in Python. I will show everything you need to know to understand the now and predict the future.

Forecasting is always sexy - knowing what will happen usually drops jaws and earns admiration. On top, it is fundamental in the business world. Companies always provide Revenue growth and EBIT estimates, which are based on forecasts. Who is doing them? Well, that could be you.

Master the Intuition Behind Forecasting Models

No need to get bogged down in complex math. This course emphasizes understanding the why behind each model. We simplify the concepts with clear explanations, intuitive visuals, and real-world examples—focusing on what really matters so you can apply these techniques confidently.

Comprehensive Coverage of Cutting-Edge Techniques

You’ll dive deep into the most advanced and sought-after time series forecasting methods that are crucial in today’s data-driven world:

Exponential Smoothing & Holt-Winters: Perfect for handling trends and seasonality in your data.
Advanced ARIMA Models (SARIMA & SARIMAX): Master these foundational models and learn how to incorporate external variables for enhanced forecasts.
Facebook Prophet: Make robust, high-accuracy forecasts with minimal data preparation.
Temporal Fusion Transformers (TFT): Leverage state-of-the-art deep learning techniques to forecast multiple time series with high accuracy.
LinkedIn Silverkite: Understand and apply this powerful, flexible model for accurate predictions in various contexts.
N-BEATS: Utilize cutting-edge neural network models for handling a variety of time series forecasting challenges.
GenAI with Amazon Chronos: Explore how generative AI is revolutionizing forecasting with models like Amazon Chronos.

Code Python Together, Line by Line

We’ll code together, ensuring you understand each step of the process. From data preparation to model implementation, you’ll learn how to write and refine every line of Python code needed to master these forecasting techniques.

Practice, Practice, Practice

Each lesson includes hands-on challenges and case studies, allowing you to immediately apply what you’ve learned. You’ll work with real datasets, solving real-world problems, and solidifying your skills through practical application.

Are You Ready to Predict the Future?

Did I spike your interest? Join me and learn how to predict the future.

What's inside

Learning objectives

Understand the fundamental principles of time series data and its significance in forecasting across various industries.
Differentiate between various time series forecasting models such as exponential smoothing, arima, and prophet, identifying when to use each model.
Apply exponential smoothing and holt-winters methods to seasonal and trend-based time series data to create accurate forecasts.
Implement sarima and sarimax models in python, incorporating external variables to enhance the predictive power of your forecasts.
Develop time series models using advanced techniques such as temporal fusion transformers (tft) and n-beats to handle complex datasets.

Optimize forecasting models by tuning parameters and using ensemble methods to improve accuracy and reliability.
Evaluate the performance of different forecasting models using metrics such as mae, rmse, and mape, ensuring the robustness of your predictions.
Code python scripts to automate the entire time series forecasting process, from data preprocessing to model deployment.
Implement deep learning models such as rnn and lstm to accurately forecast complex time series data, capturing long-term dependencies.
Develop and optimize advanced forecasting solutions using generative ai techniques like amazon chronos, incorporating state-of-the-art methods.

Understand the fundamental principles of time series data and its significance in forecasting across various industries.
Differentiate between various time series forecasting models such as exponential smoothing, arima, and prophet, identifying when to use each model.
Apply exponential smoothing and holt-winters methods to seasonal and trend-based time series data to create accurate forecasts.
Implement sarima and sarimax models in python, incorporating external variables to enhance the predictive power of your forecasts.
Develop time series models using advanced techniques such as temporal fusion transformers (tft) and n-beats to handle complex datasets.
Optimize forecasting models by tuning parameters and using ensemble methods to improve accuracy and reliability.
Evaluate the performance of different forecasting models using metrics such as mae, rmse, and mape, ensuring the robustness of your predictions.
Code python scripts to automate the entire time series forecasting process, from data preprocessing to model deployment.
Implement deep learning models such as rnn and lstm to accurately forecast complex time series data, capturing long-term dependencies.
Develop and optimize advanced forecasting solutions using generative ai techniques like amazon chronos, incorporating state-of-the-art methods.

Syllabus

Time Series Analysis and Forecasting with Python

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Course Introduction

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Diogo's Introduction and Background

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PART 1 - TIME SERIES ANALYSIS

Time Series Analysis Overview

Overview of the Python Programming Crash Course

Game Plan for Introduction to Time Series Forecasting

What is Time Series Data?

Python - Libraries and Data

Python - Time Series Index

Python - Exploratory Data Analysis

Python - Data Visualization

Python - Data Manipulation

Seasonal Decomposition

Python - Seasonal Plots

Python - Seasonal Decomposition

Auto-Correlation

Python - Auto-correlation

Partial Auto-Correlation

Python - Partial Auto-Correlation

Python - Building a Useful Function Script

Can you predict stock prices?

What did we learn in this section?

CASE STUDY: Forecasting Gone Wrong

Will you help me?

Exponential Smoothing & Holt-Winters

Game Plan For Exponential Smoothing and Holt-Winters

CASE STUDY BRIEFING: Customer Complaints

Python - Set Up

Python - Data Processing

Training and Test Set in Time Series

Python - Training and Test Set

Simple Exponential Smoothing

Python - Simple Exponential Smoothing

Double Exponential Smoothing

Python - Double Exponential Smoothing

Triple Exponential Smoothing aka Holt-Winters

Python - Triple Exponential Smoothing aka Holt-Winters

Measuring Errors for Time Series Forecasting

Python - MAE, RMSE, MAPE

Python - Predicting The Future

Python - Daily Data

Python - Working on the Useful Code Script

Holt-Winter Pros and Cons

HOLT-WINTERS CAPSTONE PROJECT: Air miles

Project Presentation: Air miles

Python Solutions: Setting up and EDA

Python Solutions: Model building and Assessment

ARIMA, SARIMA and SARIMAX

Game Plan for ARIMA, SARIMA and SARIMAX

CASE STUDY BRIEFING: Predicting Daily Revenues

Python - Setting Up

ARIMA

Auto-Regressive

Integrated

Python - Stationarity with ChatGPT

Moving Average

Python - ARIMA

AIC and BIC

SARIMA

Python - SARIMA

SARIMAX

Python - SARIMAX

Cross-Validation for Time Series

Python - Cross-Validation

Parameter Tuning

Python - Setting the Parameters

Python - Parameter Tuning

Python - Parameter Tuning Results

Python - Predicting The Future Set Up

SARIMAX Pros and Cons

PART 2: MODERN TIME SERIES FORECASTING

Modern Time Series Forecasting Overview

(Facebook) Prophet

Game Plan for Facebook Prophet

Structural Time Series and Prophet

CASE STUDY BRIEFING: Bike Sharing

Python - Directory and Libraries

Python - Preparing Data

Dynamic Holidays

Python - Holidays

Prophet Model Parameters

Python - Prophet Model

Python - Regressor Coefficients with ChatGPT

Python - Performance Metrics

Python - Fixing 2012-10-29 with ChatGPT

Python - Feature Engineering

Python - Parameter Tuning Set Up

Python - Parameter Tuning Outcome

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Master Time Series Analysis and Forecasting with Python 2025 with these activities:

Review Statistical Concepts

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Reinforce your understanding of fundamental statistical concepts to better grasp the underlying principles of time series analysis.

Browse courses on Statistical Analysis

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Review descriptive statistics concepts.
Practice hypothesis testing examples.
Study regression analysis techniques.

Read 'Forecasting: Principles and Practice'

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Gain a deeper understanding of forecasting principles and practices by studying a widely respected textbook in the field.

View Forecasting: Principles and Practice on Amazon

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Obtain a copy of the book.
Read the chapters relevant to the course topics.
Work through the examples and exercises.

Implement Time Series Models on Kaggle Datasets

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Solidify your understanding of time series models by applying them to real-world datasets and comparing their performance.

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Find time series datasets on Kaggle.
Preprocess the data and prepare it for modeling.
Implement various time series models.
Evaluate the performance of each model.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Write a Blog Post on Time Series Forecasting

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Reinforce your understanding by explaining time series concepts and models in a clear and concise manner.

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Choose a specific topic.
Research the topic thoroughly.
Write a clear and concise blog post.
Publish the blog post.

Develop a Forecasting Dashboard

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Apply your knowledge to build a comprehensive forecasting dashboard that visualizes time series data and model predictions.

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Choose a time series dataset.
Develop forecasting models.
Design and implement a dashboard.
Deploy the dashboard.

Read 'Time Series Analysis with Applications in R'

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Supplement your learning with a book that provides a strong theoretical foundation and practical examples.

View Time Series Analysis: With Applications in R... on Amazon

Show steps

Obtain a copy of the book.
Read the chapters relevant to the course topics.
Adapt the R examples to Python.

Contribute to a Time Series Forecasting Library

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Deepen your understanding by contributing to an open-source project related to time series forecasting.

Show steps

Find an open-source library.
Identify areas for contribution.
Contribute code or documentation.

Career center

Learners who complete Master Time Series Analysis and Forecasting with Python 2025 will develop knowledge and skills that may be useful to these careers:

Forecasting Analyst

A Forecasting Analyst is responsible for developing and implementing forecasting models to predict future trends and outcomes. This work enables organizations to make informed decisions based on data driven insights. This course helps build a foundation for those aspiring to become Forecasting Analysts by providing experience with a wide range of forecasting models, including Exponential Smoothing, ARIMA, and Facebook Prophet. The practical coding exercises in Python, along with real-world case studies, enhances the ability to apply these models effectively. The course's comprehensive coverage of advanced techniques, such as Temporal Fusion Transformers and Amazon Chronos, positions learners to tackle complex forecasting challenges. A Forecasting Analyst should consider this course, since it has been 100% remade and updated.

See salaries and explore the career path for Forecasting Analyst

Quantitative Analyst

Quantitative Analysts develop and implement mathematical and statistical models for financial analysis and risk management. This course is highly relevant for Quantitative Analysts, particularly the sections on time series analysis and forecasting models. The course covers advanced forecasting models, including Exponential Smoothing, ARIMA, and Facebook Prophet. The hands-on coding approach in Python and the real-world case studies are very helpful for applying these models effectively. The comprehensive code examples using libraries and data is relevant to a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Financial Analyst

Financial Analysts use forecasting models to predict future financial performance and market trends. This course is highly relevant for Financial Analysts, as it covers essential time series forecasting techniques, including Exponential Smoothing, ARIMA, and Facebook Prophet. The hands-on coding approach in Python allows Financial Analysts to implement these models practically. The course's coverage of advanced techniques such as Temporal Fusion Transformers and Amazon Chronos prepares analysts for more complex forecasting scenarios. A Financial Analyst would learn to code Python together line by line.

See salaries and explore the career path for Financial Analyst

Supply Chain Analyst

Supply Chain Analysts optimize supply chain operations by forecasting demand and managing inventory levels. This course can be very helpful for Supply Chain Analysts, as it provides skills in time series forecasting to predict demand patterns and optimize inventory. The course covers a range of forecasting models, including Exponential Smoothing, ARIMA, and Facebook Prophet. The practical coding exercises in Python and real-world case studies enhance the ability to apply these models effectively. The hands-on challenges will allow Supply Chain Analysts to immediately apply what they’ve learned.

See salaries and explore the career path for Supply Chain Analyst

Investment Analyst

Investment Analysts evaluate investment opportunities and provide recommendations to clients or organizations. This course's focus on forecasting models is highly relevant for Investment Analysts, as it helps them predict market trends and assess investment opportunities. The course covers various forecasting models, including Exponential Smoothing, ARIMA, and Facebook Prophet. The practical coding exercises in Python are helpful for applying these models effectively. An aspiring Investment Analyst can learn how to automate the entire time series forecasting process, from data preprocessing to model deployment.

See salaries and explore the career path for Investment Analyst

Actuary

Actuaries assess and manage financial risks using statistical modeling and forecasting techniques. This course is highly relevant for Actuaries, particularly the sections on time series analysis and forecasting models. The course covers a range of forecasting models, including Exponential Smoothing, ARIMA, and Facebook Prophet. The hands-on coding approach in Python and the real-world case studies enhance the ability to apply these models effectively. All lessons include hands-on challenges and case studies.

See salaries and explore the career path for Actuary

Risk Analyst

Risk Analysts identify and assess potential risks to an organization and develop strategies to mitigate those risks. This course's focus on forecasting models helps Risk Analysts predict potential risks and develop mitigation strategies. The course covers techniques such as Exponential Smoothing, ARIMA, and Facebook Prophet. The practical coding exercises in Python and real-world case studies are helpful for applying these models effectively. By taking this course, a Risk Analyst can develop time series models using advanced techniques such as Temporal Fusion Transformers and N-BEATS to handle complex datasets.

See salaries and explore the career path for Risk Analyst

Market Research Analyst

Market Research Analysts analyze market trends and consumer behavior to provide insights for product development and marketing strategies. This course's focus on time series analysis helps Market Research Analysts forecast market trends and predict consumer behavior. The course provides a comprehensive coverage of forecasting models, including Exponential Smoothing, ARIMA, and Facebook Prophet. The practical coding exercises in Python and real-world case studies are useful in applying these models effectively. Many Market Research Analysts are interested in predicting the future, which is the goal of this course.

See salaries and explore the career path for Market Research Analyst

Data Scientist

Data Scientists utilize statistical methods and machine learning algorithms to analyze data and extract actionable insights. Time series analysis and forecasting are critical components of a Data Scientist's toolkit, especially in industries such as finance, retail, and supply chain management. This course may be useful in developing skills in time series forecasting, covering methods like Exponential Smoothing, ARIMA, and more advanced techniques such as Temporal Fusion Transformers. The hands-on coding approach in Python and the focus on real-world applications helps one apply these techniques effectively. A Data Scientist who takes this course can expect to improve their ability and confidence to make predictions, thanks to updates in neural networks and Amazon AutoGluon.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers develop and deploy machine learning models to solve complex problems. Time series forecasting is an important application of machine learning, and this course is relevant for Machine Learning Engineers looking to deepen their understanding of forecasting techniques. The course covers advanced models like Temporal Fusion Transformers (TFT) and N-BEATS, which are cutting-edge deep learning techniques for time series forecasting. The practical coding exercises in Python enable Machine Learning Engineers to implement and optimize these models. This course provides code walkthroughs, ensuring you understand each step of the process.

See salaries and explore the career path for Machine Learning Engineer

Business Intelligence Analyst

Business Intelligence Analyst roles involve analyzing market trends to provide insights that drive strategic decision-making. This course may be helpful for current and aspiring Business Intelligence Analysts by providing them with skills in time series analysis and forecasting. This course covers various essential forecasting models, including Exponential Smoothing, ARIMA, and Facebook Prophet. The practical coding exercises in Python and real-world case studies enable learners to apply these models effectively. The addition of modern techniques such as Temporal Fusion Transformers and Amazon Chronos is valuable in tackling complex forecasting challenges. This course also places an emphasis on simplifying complex math for better intuition.

See salaries and explore the career path for Business Intelligence Analyst

Economist

Economists analyze economic data and develop models to forecast economic trends and inform policy decisions. This course can be useful for aspiring and current Economists by providing them with skills in time series analysis and forecasting. The course covers various forecasting models, including Exponential Smoothing, ARIMA, and Facebook Prophet. The practical coding exercises in Python and real-world case studies may enhance the ability to apply these models effectively. The inclusion of advanced techniques such as Temporal Fusion Transformers and Amazon Chronos is valuable. Many Economists will appreciate that this course emphasizes understanding the why behind each model.

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Operations Analyst

Operations Analysts improve organizational efficiency by analyzing operational data and developing optimization strategies. This course may be helpful for Operations Analysts as it provides them with skills in time series forecasting to predict operational trends and optimize resource allocation. The course covers various forecasting models, including Exponential Smoothing, ARIMA, and Facebook Prophet. The practical coding exercises in Python and real-world case studies enhance the ability to apply these models effectively. This course offers hands-on challenges and case studies, allowing you to immediately apply what you’ve learned.

See salaries and explore the career path for Operations Analyst

Bioinformatician

Bioinformaticians analyze biological data using computational tools and statistical methods. Time series analysis can be applied to genomic data, so this course could be useful in developing skills in time series forecasting. The course describes techniques such as Exponential Smoothing, ARIMA, and Facebook Prophet. The practical coding exercises in Python and real-world case studies enhance the ability to apply these models effectively. By taking this course, Bioinformaticians will know how to evaluate the performance of different forecasting models.

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Data Engineer

Data Engineers build and maintain the infrastructure required for data storage, processing, and analysis. While not directly focused on modeling, this course may be useful as it provides Data Engineers with a better understanding of the data requirements and preprocessing steps involved in time series forecasting. The course covers topics such as data manipulation and feature engineering, which are essential skills for Data Engineers. The knowledge gained from this course helps Data Engineers better support data scientists and analysts working on forecasting projects. Those working as Data Engineers will find the library requirements.txt file for all sections added.

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Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Master Time Series Analysis and Forecasting with Python 2025.

Forecasting

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Provides a comprehensive overview of forecasting methods, from classical to modern approaches. It is particularly useful for understanding the theoretical underpinnings of the models covered in the course. The book also includes numerous examples and case studies that illustrate the application of these methods in practice. It serves as an excellent reference for both beginners and experienced practitioners.

Forecasting: Principles and Practice

Paperback

$$$

Forecasting: principles and practice

Paperback

$$$

Forecasting: principles and practice

Paperback

$$$

Time Series Analysis

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Provides a rigorous treatment of time series analysis, with a focus on practical applications using R. While the course uses Python, the underlying concepts are transferable. The book is particularly strong on the theoretical foundations of ARIMA models and spectral analysis. It valuable resource for students who want to delve deeper into the mathematical aspects of time series analysis.

Time Series Analysis: With Applications in R...

Hardcover

$$$

Master Time Series Analysis and Forecasting with Python 2025

What's inside

Learning objectives

Syllabus

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Activities

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Reading list

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