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
4.72 (955 reviews)
8,051
students
37.5 hours
content
Mar 2025
last update
$74.99
regular price
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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udemy ID
4/28/2021
course created date
5/31/2021
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