Modern Time Series Forecasting

Modern forecasting moves beyond single-model approaches to handle large, complex panels of time series with diverse patterns and uncertainties. Effective workflows combine statistical, machine learning, and deep learning models, emphasizing scalability, probabilistic accuracy, and reproducibility.

Global models leverage information across series, while ensembles and foundation models enhance robustness and automation. Agentic systems now orchestrate the full forecasting lifecycle—from data preparation to monitoring—with built-in compliance and efficiency.

This course teaches how to build scalable, interpretable, and production-ready forecasting systems, integrating modern algorithms, rigorous evaluation, and sound methodological principles.

Statistical Learning, Machine Learning & Artificial Intelligence

This course provides a comprehensive introduction to statistical learning, machine learning, and artificial intelligence, bridging traditional statistical modeling with modern algorithmic approaches. Participants will learn how models learn from data, how to evaluate and interpret results, and how to apply these techniques to real-world problems.

The course teaches how to build end-to-end workflows using R production-ready frameworks like tidymodels, ensuring consistency, reproducibility, and scalability from model development to deployment.

By the end, participants will be able to design, tune, and assess predictive models, integrate them into robust analytical pipelines, and confidently apply state-of-the-art learning techniques across domains in business, science, and technology.

R Coding

This course provides a comprehensive introduction to R programming, designed for students, researchers, and professionals who want to develop a solid foundation in data analysis, statistical computing, and reproducible research.

The course begins with the basics of the R environment, syntax, and data structures, then progresses through data wrangling with the tidyverse, data visualization with ggplot2, and functional programming with purrr. Learners will also gain hands-on experience in data import and export, data cleaning, and workflow automation using reproducible tools such as R Markdown and Quarto.

The course also introduces Business Intelligence (BI) development using R, where learners will build interactive dashboards and data applications with Flexdashboard and Shiny. These tools enable the creation of dynamic visual analytics and web-based reports directly from R, empowering participants to share insights interactively with non-technical stakeholders and decision-makers.

Business Consulting

I offer consultancy and training about forecasting, machine learning, and the development of business specific statistical models.

I helped several companies, like Luxottica, Mediaworld, and Blue Panorama Airlines, to improve their forecasting processes and tools. I also helped companies to train their employees on how to use R and Python for data analysis and forecasting. The highly intensive training allows companies to help their specialists in the process of becoming proficient forecasters and forecasting model developers.

For more details, email me at zanottimarco17@gmail.com.

Private Lectures

I teach econometrics, time series, forecasting, R and Python programming, statistics and data science related topics to students.
I have helped more than 300 students preparing their exams, thesis and projects.

If you need help, do not hesitate! Contact me directly emailing to zanottimarco17@gmail.com or texting me on WhatsApp.

Reviews on my teaching support can be found on Superprof: