Forecast App

The Forecast App is a comprehensive tool designed to guide users through the entire forecasting workflow, from raw data to scenario analysis. It is tailored for both beginners and experienced practitioners, allowing the exploration, modeling, and explanation of time series data without requiring advanced knowledge of forecasting techniques.

The app is organized into four main sections — Data, Analyze, Features, and Forecast — which collectively enable users to:

  • Upload and explore time series data
  • Detect and handle missing values and anomalies
  • Transform and preprocess data for improved forecasting performance
  • Create and select relevant features, both internal and external
  • Fit and optimize a wide range of forecasting models, including classical, machine learning, deep learning, and ensemble approaches
  • Evaluate, compare, and explain the models’ predictions
  • Generate probabilistic forecasts and business-oriented scenarios

The app provides great flexibility in analysis: interactive visualizations, configurable model parameters, and multiple options for evaluation and explanation allow users to tailor the process to their specific needs.

You can access the latest version at Forecast App.

Follow the user guide to leverage the full potential of the Forecast App to obtain accurate forecasts, understand model behavior, and explore different scenarios for informed decision-making.

Feel free to share the Forecast App on LinkedIn if you find it useful and remember to tag me!

If you find bugs or have suggestions for improvements, please open an issue on GitHub.

The source code of the Forecast App is available on GitHub under the MIT license.

Utilsforecast - Nixtla

I am an active contributor of the open source Nixtla’s library utilsforecast.

My contributions are mainly focused on expanding the loss functions available to allow for a more comprehensive and flexible evaluation of forecasting models.

In particular, I created some well-know metrics for evaluating point forecasts, the Mean Squared Scaled Error (MSSE) and the Root Mean Squared Scaled Error (RMSSE), and some metrics for probabilistic forecasting evaluation, the Scaled Quantile Loss (SQL) and the Scaled Multi-Quantile Loss (SMQL).

Disposition Effect

The disposition effect is a joint research project on the topic of Behavioral Economics & Finance developed by me and Lorenzo Mazzucchelli.

The disposition effect is an irrational behavior in financial markets consisting in the realization that investors are more likely to sell an asset when it is gaining value compared to when it is losing value. A phenomenon which is closely related to sunk costs’ bias, diminishing sensitivity, and loss aversion.

The research project aims at studying the disposition effect more deeply and and more effectively compared to what it is already been done by other researchers.
The project ends with the publication of a scientific paper and an open source software:

  • - Mazzucchelli, L., Zanotti, M., et al. (2026). Do Short Exposure and Systematic Risk Exposure Drive Asymmetries in the Disposition Effect? arXiv

  • dispositionEffect, R Software, CRAN

The software has been presented at the useR!2021 Conference, and it has been listed on the Top 40 New CRAN Packaes on August 2021.

Silene Acaulis

The Silene acaulis is a collaboration project on the topic of Ecological Science developed by Maria Elisa Pierfederici. I have contributed to the research with the statistical analysis of biological data.

Climate is an important determinant of plant population growth rates; however, different components of climate may have contrasting effects on both plant population growth rates and individual plant vital rates. Thus, it is necessary to evaluate multiple climatic drivers simultaneously to understand how climate may affect plant population survival and persistence. The project focused on the alpine cushion plant Silene acaulis, a foundational plant widely distributed in the Northern hemisphere that facilitates other species by creating microclimatic habitats.

The project ends with the publication of a scientific paper:

  • Pierfederici, M. E., et al. (2026), Climate changing’s effects on Silene acaulis. arXiv.