- Docente responsabile
- FABIO DERCOLE
- CCS proponenti
- Ingegneria Matematica Ingegneria per l'Ambiente e il Territorio|Ingegneria Fisica|Ingegneria Biomedica|Ingegneria Gestionale|Ingegneria dell'Automazione|Ingegneria Informatica
- CFU
- 2
- Ore in presenza
- 28
- Prerequisiti
- Essentially no specific knowledge or skill is required, because of the tutorial style of the mini-course. Basic knowledge of dynamical systems, linear and nonlinear, in both continuous and discrete time, and coding skills in matlab/python are definitely useful for a proficient participation.
- N° max studenti
- 20
- Criteri di selezione
- GPA and credits earned from completed exams.
- Parole chiave:
- Artificial Neural Networks, Deterministic chaos, Multi-step forecasting, Nonlinear time series
- Tag
- Finance, Computer science, Engineering, Artificial intelligence
Descrizione dell'iniziativa
The activity is a mini-course with tutorial/practical cross-disciplinary focus on the design, training, and testing of Artificial Intelligence (AI) models for the forecasting of complex oscillating time series. Participants will be introduced to basic concepts of model identification and forecasting of temporal data series, specifically adopting linear auto-regressive (AR) models to fix a benchmark and Feed-Forward (FF) and recursive (R) Neural Networks (NN) to advance the forecasting accuracy and robustness with AI-powered nonlinearities.
When predicting one-step ahead, AR models are the first “cheap” choice, grounded on the efficient least squares method. FFNNs add nonlinearities, though still offering a “static” approach that considers past samples as independent inputs, and losing efficiency and accuracy over long regressive horizons. RNNs are the edge-cutting technology. They fully exploit the data temporal dimension, thanks to an internal state that is updated using the same efficient recursive rule over the input series.
When predicting multiple steps ahead, the recursive use of one-step predictors is not the best practice, especially on series that show a sensitivity to perturbations typical of random-like (i.e., chaotic) oscillating data. While a multi-model approach (one model for each step ahead to be predicted) is possible for both the AR and NN frameworks, NNs offer the easy and efficient multi-output alternative, where the same model is trained to optimize the forecasts over an horizon of future steps.
Participants will be guided step-by-step through these methodologies (AR, FNN, RNN, single-step, multi-step recursive, multi-model, multi-output) and their pipeline of implementation, from the configuration of the relevant tools (only freeware tools will be used, like PyTorch and TensorFlow), to the end-product: the one- or multi-step predictor. Didactical case studies, based on artificial data generated by simulating known dynamical models, will be used to present and compare the methodologies, and proposed as exercises alike. Real-world case studies, based on data series in the fields of natural and environmental sciences, civil and environmental engineering, bio-medical engineering, and finance will be used to showcase the descriptive power of the presented methodologies.
The mini-course will consist of 10 tutorial meetings of 120 min (for an equivalent total of 26.67 45-min lectures) in person in a room at PoliMi, campus Città-Studi, equipped with wifi access and power outlets. Participants are requested to use their own laptop (Windows, Mac, Linux). The meeting will be recorded and streamed through the Webex platform for students remotely connected from the foreign campus of the ENHANCE network. Recordings will be made temporarily available to all participants.
The activity will be co-designed with:
- one of the EU-leading group on AI-based analysis of natural and environmental data series (the Chair of Smart Water Management at the Technische Universität Berlin – Einstein Center Digital Future), with long-lasting experience on both short-term high-frequency (hourly, daily) and historical data;
- a group of computer scientists in the Faculty of Electronics and Information Technology at the Warsaw University of Technology, EU-leading experts in genomic data.
Biophysical and financial data series will be selected with the help of biomedical and management colleagues in the PoliMi campus. Mostly publicly available data will be used.
Lectures and technical/practical notes will be provided to participants. The reference for further reading is "Deep Learning in Multi-step Prediction of Chaotic Dynamics: From Deterministic Models to Real-world Systems", by M. Sangiorgio, F. Dercole, G. Guariso, Springer 2022.
Periodo di svolgimento
dal April 2026 a April 2026
Calendario
20-23 e 27-30 aprile, ore 16:30-19:45 (in presenza)Totale di 8 incontri di 4 ore da 45 minuti (+pausa di 15 minuti nel mezzo)