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Udemy

Master Time Series Analysis and Forecasting with Python 2026

via Udemy

Overview

Time Series with Deep Learning (LSTM, TFT, N-BEATS), GenAI (Amazon Chronos), Prophet, Silverkite, ARIMA. Demand Forecast

What you'll learn:
  • 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.

Updates September 2025:

  • All the Darts library sections were re-recorded.

  • New sections on Intermittent Time Series and Classification for Time Series.

  • New Projects!

  • More Concise videos by coding with GenAI.

Updates August 2025:

  • New AI Course Assistant is live!

Updates July 2025:

  • Fully updated the exercises in the section Python Essentials.

Updates March 2025:

  • Google TSMixer Launched

  • Introduction to Time Series Analysis and Exponential Smoothing Python tutorials remade.

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 you 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!


WHY SHOULD YOU ENROLL IN THIS COURSE?

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 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 – Handle trends and seasonality with elegance.

  • Advanced ARIMA Models (SARIMA & SARIMAX) – Incorporate external variables for enhanced forecasts.

  • Facebook Prophet – Robust, high-accuracy forecasts with minimal data prep.

  • Temporal Fusion Transformers (TFT) – State-of-the-art deep learning for multiple time series.

  • LinkedIn Silverkite – Flexible, powerful forecasting across contexts.

  • N-BEATS – Cutting-edge neural networks for diverse forecasting challenges.

  • GenAI with Amazon Chronos – Discover how generative AI is revolutionizing forecasting.

  • Google TSMixer (NEW) – Leverage Google’s breakthrough architecture for time series.

  • Amazon AutoGluon (NEW) – Automate high-performance forecasting pipelines.

  • Intermittent Time Series (NEW) – Tackle irregular, sporadic patterns with specialized techniques.

  • Classification for Time Series (NEW) – Expand beyond forecasting into predictive categorization.


Code Python Together, Line by Line

We’ll code side by side, ensuring you understand every step.

From data preparation to model implementation, you’ll learn how to write and refine each line of Python code needed to master these forecasting techniques.


Practice, Practice, Practice

Each lesson includes hands-on challenges and case studies, from sales to demand forecasting

You’ll apply what you’ve learned to real datasets, solve real-world problems, and solidify 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!

Syllabus

  • Time Series Analysis and Forecasting with Python
  • PART 1 - TIME SERIES ANALYSIS
  • Introduction to Time Series Forecasting
  • Time Series Analysis Practice
  • Exponential Smoothing & Holt-Winters
  • HOLT-WINTERS CAPSTONE PROJECT: Air miles
  • ARIMA, SARIMA and SARIMAX
  • PART 2: MODERN TIME SERIES FORECASTING
  • (Facebook) Prophet
  • CAPTONE PROJECT: Prophet
  • Intermittent Time Series
  • Mid-course Feedback
  • PART 3 - DEEP LEARNING FOR TIME SERIES FORECASTING
  • RNN - LSTM
  • LSTM - Multiple Time Series Forecasting
  • Temporal Fusion Transformers (TFT)
  • CAPSTONE PROJECT: Multiple Series with TFT
  • N-BEATS
  • PART 4 - ADVANCED CONTENT FOR TIME SERIES FORECASTING
  • GenAI for Time Series: Amazon Chronos
  • Amazon AutoGluon
  • Google TSMixer
  • Classification for Time Series
  • End of Course Feedback
  • Time Series Analysis Graveyard
  • Linkedin Silverkite
  • CAPSTONE PROJECT: Build an Automated Time Series Forecasting Model
  • APPENDIX - Python for Data Analysis Course
  • Python Essentials
  • Book Review
  • Variable Types and Operators
  • If-else and Conditionals
  • Python Intermediate
  • CAPSTONE PROJECT: Virtual Escape Game
  • Pandas
  • Pandas Challenge
  • Bonus Section

Taught by

Diogo Alves de Resende

Reviews

4.5 rating at Udemy based on 1471 ratings

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