PREDICTIVE BUSINESS AND FINANCE
- Anno accademico
- 2021/2022 Programmi anni precedenti
- Titolo corso in inglese
- PREDICTIVE BUSINESS AND FINANCE
- Codice insegnamento
- EM1415 (AF:339192 AR:180105)
- 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
- 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. Use smoothing methods with time series data (moving average, exponential smoothing)
4. Understand the different components of time series data5
5. Use regression methods for forecasting
2. Specify appropriate metrics to assess forecasting models
3. Use smoothing methods with time series data (moving average, exponential smoothing)
4. Understand the different components of time series data5
5. Use regression methods 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. Regression models
2. Time series models: ARIMA models.
3. Dynamic regression models
4. Advanced forecasting methods
2. Time series models: ARIMA models.
3. Dynamic regression models
4. Advanced forecasting methods
Testi di riferimento
Hyndman, R. J. and G. Athanasopoulos (2018): Forecasting: Principles and Practice (2nd Edition). https://otexts.com/fpp2/
Modalità di verifica dell'apprendimento
By way of evaluation, two (i.e. mid and end of course) 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 homework and a final examination. The final grade 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
Programma definitivo.
Data ultima modifica programma: 13/09/2021