PREDICTIVE BUSINESS AND FINANCE

Anno accademico

2022/2023 Programmi anni precedenti

Titolo corso in inglese

PREDICTIVE BUSINESS AND FINANCE

Codice insegnamento

EM1415 (AF:358757 AR:188852)

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 data
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

Contenuti

1. Regression models
2. Time series models: ARIMA models.
3. Dynamic regression models
4. Advanced forecasting methods

Testi di riferimento

Hyndman, R. J. and G. Athanasopoulos (2021): Forecasting: Principles and Practice (3rdd Edition). https://otexts.com/fpp3/

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

Obiettivi Agenda 2030 per lo sviluppo sostenibile

Questo insegnamento tratta argomenti connessi alla macroarea "Cambiamento climatico e energia" e concorre alla realizzazione dei relativi obiettivi ONU dell'Agenda 2030 per lo Sviluppo Sostenibile

Il programma è ancora provvisorio e potrà subire modifiche.

Data ultima modifica programma: 15/05/2022