Master time series forecasting from the ground up through one cohesive, real-world project: predicting global semiconductor chip sales and NVIDIA stock prices. This hands-on course takes you through the complete forecasting workflow—acquiring data from APIs and public sources, wrangling and engineering features, running EDA, and building models that actually ship. You'll implement the full spectrum of techniques: classical statistical models (ARIMA, SARIMA, SARIMAX, Prophet), tree-based machine learning (XGBoost, LightGBM with Optuna tuning), and deep learning architectures (LSTM, GRU, CNN-LSTM, Temporal Fusion Transformers). Go further with multivariate analysis using Granger causality, VAR, and VECM to uncover how chip sales and stock prices influence each other, then combine everything into ensemble and hybrid pipelines. Finally, deploy your best model as a live FastAPI endpoint and an interactive Streamlit dashboard, complete with automated retraining and cloud deployment. Across 4 modules and 48 concise videos, you'll build a portfolio-ready, end-to-end forecasting system that demonstrates production-grade skills employers value.

Time Series Forecasting with Python: Models to Production
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Time Series Forecasting with Python: Models to Production
This course is part of Applied Machine Learning: Tabular, Forecasting & Deployment Specialization

Instructor: Board Infinity
Included with Learn more
Recommended experience
What you'll 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'll gain
- Predictive Modeling
- Machine Learning Methods
- Model Evaluation
- Model Deployment
- Forecasting
- Recurrent Neural Networks (RNNs)
- Deep Learning
- Data Preprocessing
- Statistical Modeling
- Machine Learning
- Statistical Analysis
- Statistical Methods
- MLOps (Machine Learning Operations)
- Model Training
- Feature Engineering
- Supervised Learning
- Applied Machine Learning
- Data Wrangling
- Time Series Analysis and Forecasting
Tools you'll learn
Details to know

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June 2026
16 assignments
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Board Infinity

Board Infinity
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