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
Product Brand: Udemy
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Master Time Series Analysis and Forecasting with Python 2025 Coupon Code. Time Series with Deep Learning (LSTM, TFT, N-BEATS), GenAI (Amazon Chronos), Prophet, Silverkite, ARIMA. Demand Forecast
Master Time Series Analysis and Forecasting with Python 2025
Master Time Series Analysis and Forecasting with Python 2025 Course. 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!
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.
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Who this course is for:
- Business analysts looking to improve their forecasting skills and techniques.
- Data scientists interested in applying time series analysis and forecasting to business problems.
- Marketing professionals looking to forecast future demand for products or services.
- Financial analysts seeking to forecast future trends and performance for businesses.
- Operations managers looking to improve demand planning and forecasting for their organization.
Instructor
Diogo Alves de Resende – Diogo is a data analytics and business analytics professional with years of experience in the field. He has expertise in various methodologies, including time series forecasting for predicting sales trends, econometrics for analyzing economic data, and machine learning for optimizing marketing campaigns.
His background includes working for a major e-commerce company, where he used these techniques to drive business growth, and collaborating with the United Nations on a Mobile Money project in Lesotho, where he helped increase financial inclusion in the country.
In his courses, Diogo aims to provide practical and applicable knowledge through real-life examples and datasets. For example, he often uses case studies from his own work experiences to illustrate key concepts and demonstrate their relevance in the professional world. His goal is to equip students with the skills and tools necessary to succeed in their own careers in data science.
