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
Udemy Discounts Coupon for 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
Created by Diogo Alves de Resende | 43 hours on-demand video course | 14 downloadable resources
Coupon Details
Master Time Series Analysis and Forecasting with Python 2025 Courses Coupon Code. 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.
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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