PREDICTIVE BUSINESS AND FINANCE
- Anno accademico
- 2024/2025 Programmi anni precedenti
- Titolo corso in inglese
- PREDICTIVE BUSINESS AND FINANCE
- Codice insegnamento
- EM1415 (AF:449564 AR:257643)
- Lingua di insegnamento
- Inglese
- Modalità
- In presenza
- Crediti formativi universitari
- 6
- Livello laurea
- Laurea magistrale (DM270)
- Settore scientifico disciplinare
- SECS-P/05
- Periodo
- 1° Periodo
- Anno corso
- 2
- Sede
- VENEZIA
- In collaborazione con
-
- Spazio Moodle
- Link allo spazio del corso
Inquadramento dell'insegnamento nel percorso del corso di studio
This course is one of the teaching activities of the Master's Degree Programme in "Data Analytics for Business and Society". In tandem with the educational objectives of this course, students will be exposed to data analytic techniques and methods for handling economic-financial prediction related problems. Precisely, this activity seeks to present the main mathematical and statistical tools necessary for forecasting.
Risultati di apprendimento attesi
1. Visualize time series data
2. Specify appropriate metrics to assess forecasting models
3. Introduction to basic filtering methods in the time domain (moving average, exponential smoothing)
4. Understand the structural decomposition in components of time series data
5. The use of time series models for forecasting
2. Specify appropriate metrics to assess forecasting models
3. Introduction to basic filtering methods in the time domain (moving average, exponential smoothing)
4. Understand the structural decomposition in components of time series data
5. The use of time series models for forecasting
Prerequisiti
Mathematical Tools:
Matrix Algebra
Differential Calculus
Integral Calculus
Statistical Tools:
Random Variables and Distribution Theory
Point and Interval Estimation
Hypothesis Testing
Least Squares and Standard Linear Model
Matrix Algebra
Differential Calculus
Integral Calculus
Statistical Tools:
Random Variables and Distribution Theory
Point and Interval Estimation
Hypothesis Testing
Least Squares and Standard Linear Model
Contenuti
1. Introduction to signal extraction.
2. Basic smoothing and filtering methods.
3. Time Series Regression and Distributed Lags Models.
4. Time series models: ARIMA models.
5. State Space Models.
6. Non-Linear Time Series Models. (If time allows)
2. Basic smoothing and filtering methods.
3. Time Series Regression and Distributed Lags Models.
4. Time series models: ARIMA models.
5. State Space Models.
6. Non-Linear Time Series Models. (If time allows)
Testi di riferimento
Hyndman, R. J. and Athanasopoulos, G. (2021): Forecasting: Principles and Practice (3rd Edition). https://otexts.com/fpp3/
Shumway, R. H. and Stoffer, D. S. (2017): Time Series Analysis and Its Applications, With R Examples. https://link.springer.com/book/10.1007/978-3-319-52452-8
Bee Dagum, E. and Bianconcini, S. (2016): Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation. (Ch.2-5) https://link.springer.com/book/10.1007/978-3-319-31822-6
Harvey, A. C. (1993): Time Series Models (2nd Edition). https://books.google.ge/books/about/Time_Series_Models.html?id=s1ScQgAACAAJ&redir_esc=y
Harvey, A. C. (1990): Forecasting, Structural Time Series Models and the Kalman Filter. https://books.google.it/books/about/Forecasting_Structural_Time_Series_Model.html?id=Kc6tnRHBwLcC&redir_esc=y
Shumway, R. H. and Stoffer, D. S. (2017): Time Series Analysis and Its Applications, With R Examples. https://link.springer.com/book/10.1007/978-3-319-52452-8
Bee Dagum, E. and Bianconcini, S. (2016): Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation. (Ch.2-5) https://link.springer.com/book/10.1007/978-3-319-31822-6
Harvey, A. C. (1993): Time Series Models (2nd Edition). https://books.google.ge/books/about/Time_Series_Models.html?id=s1ScQgAACAAJ&redir_esc=y
Harvey, A. C. (1990): Forecasting, Structural Time Series Models and the Kalman Filter. https://books.google.it/books/about/Forecasting_Structural_Time_Series_Model.html?id=Kc6tnRHBwLcC&redir_esc=y
Modalità di verifica dell'apprendimento
By way of evaluation, a main examinations covering both the theory and application of the concepts developed in class will be conducted. However, I will also propose an end-of-course project (homework) that examines students' capability in developing a solution to a problem without limiting themselves to the information given in class. The course grade will be based on the homework and a the final examination for the first exam in the year, and only a final examination for all the other exam sessions. The final grade for the first exam session will be determined using the following weights: 30\% Homework, 70\% final written exam.
Modalità di esame
scritto
Metodi didattici
Series of lectures on the various topics
Altre informazioni
Il corso è svolto in collaborazione con il partenariato esteso GRINS - Growing Resilient, INclusive and Sustainable, codice PE0000018, CUP H73C22000930001, avviso pubblico n. 341/2022 del Piano Nazionale di Ripresa e Resilienza (PNRR), Missione 4 - Componente 2 - Investimento 1.3, finanziato dall’Unione europea - NextGenerationEU.
All’interno del corso possono essere proposti incontri con testimoni aziendali aderenti al progetto, incentrati sullo sviluppo di conoscenze pratiche nella materia oggetto di studio, oltre che sui risultati del progetto stesso.
Questo insegnamento tratta argomenti connessi allo Spoke 4 Sustainable Finance - Work Package n. 3.
All’interno del corso possono essere proposti incontri con testimoni aziendali aderenti al progetto, incentrati sullo sviluppo di conoscenze pratiche nella materia oggetto di studio, oltre che sui risultati del progetto stesso.
Questo insegnamento tratta argomenti connessi allo Spoke 4 Sustainable Finance - Work Package n. 3.
Programma definitivo.
Data ultima modifica programma: 22/08/2024