Master Time Series Analysis and Forecasting with Python 2025

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

4.55 (1049 reviews)

Udemy

platform

English

language

Data & Analytics

What you will 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.

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Rating & Reviews

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Comidoc Review

Our Verdict

This course, "Master Time Series Analysis and Forecasting with Python 2025," offers a strong foundation in various time series forecasting methods using Python. Strengths include comprehensive coverage of techniques, ample hands-on practice opportunities, clear communication from the instructor, Diogo, and good organization. However, some areas for improvement are limited theoretical backing, lack of focus on handling missing data or zero values, inconsistent model evaluation reviews, and potential for improved code reusability. Overall, recommended as a solid course for building a basic foundation in time series forecasting, allowing you to delve into real-world applications with deeper understanding through further study and practice.

What We Liked

The course offers a comprehensive overview of time series analysis and forecasting methods, including deep learning techniques like LSTM and TFT.
Python code is provided for each model, enabling hands-on practice and allowing learners to automate the entire forecasting process.
The instructor, Diogo, is commended for his clear communication style, responsiveness to student queries, and accessibility through Discord.
Content is organized effectively with a good balance between practical implementation and theoretical understanding.

Potential Drawbacks

Some reviewers found the theoretical backing limited, suggesting that more statistical insight would be beneficial for fully grasping the concepts behind various models.
The course does not address handling missing data or zero values, which could be useful in real-world scenarios and might cause frustration.
Model evaluations lack insight on how to improve them for better results, leaving learners seeking guidance to optimize model performance.
Reviewers have pointed out that code reusability can be further emphasized, as there is a lot of copying and pasting involved while working with different models.