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Diogo Alves de Resende

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

Read more

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.

Enroll now

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.

Syllabus

Time Series Analysis and Forecasting with Python
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Course Introduction
[ACTION] Download the Course Materials
Read more
Diogo's Introduction and Background
Unlimited Updates and Enhancements 2025
Submit Your Update and Enhancement Requests Here
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

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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
Reinforce your understanding of fundamental statistical concepts to better grasp the underlying principles of time series analysis.
Browse courses on Statistical Analysis
Show steps
  • Review descriptive statistics concepts.
  • Practice hypothesis testing examples.
  • Study regression analysis techniques.
Read 'Forecasting: Principles and Practice'
Gain a deeper understanding of forecasting principles and practices by studying a widely respected textbook in the field.
Show steps
  • 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
Solidify your understanding of time series models by applying them to real-world datasets and comparing their performance.
Show steps
  • 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
Reinforce your understanding by explaining time series concepts and models in a clear and concise manner.
Show steps
  • Choose a specific topic.
  • Research the topic thoroughly.
  • Write a clear and concise blog post.
  • Publish the blog post.
Develop a Forecasting Dashboard
Apply your knowledge to build a comprehensive forecasting dashboard that visualizes time series data and model predictions.
Show steps
  • Choose a time series dataset.
  • Develop forecasting models.
  • Design and implement a dashboard.
  • Deploy the dashboard.
Read 'Time Series Analysis with Applications in R'
Supplement your learning with a book that provides a strong theoretical foundation and practical examples.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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.
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.
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.

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