Time Series Forecasting with Python: Models to Production
Completed by Vítor Sandro Rebelo de Sousa
October 28, 2026
13 hours (approximately)
Vítor Sandro Rebelo de Sousa's account is verified. Coursera certifies their successful completion of Time Series Forecasting with Python: Models to Production
What you will learn
Build and evaluate statistical models: exponential smoothing, Holt-Winters, ARIMA, SARIMA, SARIMAX, and Prophet.
Implement deep learning architectures: LSTM, GRU, CNN-LSTM hybrids, and Temporal Fusion Transformers.
Frame forecasting as supervised learning and train tree-based models with leak-free time series cross-validation.
Skills you will gain
- Category: Predictive Modeling
- Category: Python Programming
- Category: Machine Learning Methods
- Category: Statistical Modeling
- Category: Machine Learning
- Category: Statistical Analysis
- Category: Data Preprocessing
- Category: Forecasting
- Category: Recurrent Neural Networks (RNNs)
- Category: Deep Learning
- Category: Model Deployment
- Category: Model Evaluation
