Dr Livio Fenga
Senior Lecturer in Business Analytics
L.Fenga@exeter.ac.uk
3536
Streatham Court 1.74
Streatham Court, University of Exeter, Rennes Drive, Exeter, EX4 4PU, UK
Overview
Before joining the University of Exeter Business School in September 2021, Livio Fenga worked for 25 years as a researcher at the Italian National Institute of Statistics (ISTAT). He received two PhDs, one in Econometrics and Empirical Economics (University of Rome “Tor Vergata”) and one in Economic Statistics (University of Rome “Sapienza”). He spent several years in USA, at the Department of Applied Physics and Mathematics (APM) of the University of California San Diego (San Diego, CA) as a visiting scholar, where he earned the position of post-doctoral researcher in 2012. He subsequently (2014) received another postdoctoral degree at the Lawrence Berkeley National Laboratory (Berkeley, CA).
Research
Research interests
- Time series Analysis
- Forecasting
- Resampling methods
Livio has done extensive research in teim series forecasting and time series model selection including computer-intensive methods for model order determination in the linear and non-linear case.
Current research:
- Development of a forecasting procedure, designed to exploit external information of the type Big Data, on complex, possibly non-linear, time series
- Stochastic models of emerging or re-emerging infectious diseases
Publications
Journal articles
Fenga L, Galli M (In Press). Impact estimation on COVID-19 infections following school reopening in September 2020 in Italy. Journal of Statistics and Computer Science
Fenga L (In Press). Loss of Fitting and Distance Prediction in Fixed vs Updated ARIMA Models. Global Journal of Science Frontier Research Volume XVII Issue. Year 2017 Abstract.
Fenga L, Son-Turan S (2022). Forecasting youth unemployment in the aftermath of the COVID-19 pandemic: the Italian case. International Journal of Scientific and Management Research, 05(01), 75-91.
Fenga L, Del Castello C (2021). COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped Data. Journal of Probability and Statistics, 2021, 1-7. Abstract.
Fenga L (2021). CoViD-19: an automatic, semiparametric estimation method for the population infected in Italy. PeerJ, 9, e10819-e10819. Abstract.
Fenga L (2021). Forecasting the COVID-19 Diffusion in Italy and the Related Occupancy of Intensive Care Units. Journal of Probability and Statistics, 2021, 1-9.
Fenga L, Gaspari M (2021). Predictive Capacity of COVID-19 Test Positivity Rate. Sensors, 21(7), 2435-2435. Abstract.
Quattrociocchi L, Tibaldi M, Marsili M, Fenga L, Caputi M (2020). Active Ageing and Living Condition of Older Persons Across Italian Regions. Journal of Population Ageing, 14(1), 91-136.
Fenga L (2019). Filtering and prediction of noisy and unstable signals: the case of Google Trends data. Journal of Forecasting, 39(2), 281-295. Abstract.
Cerqueti R, Fenga L, Ventura M (2018). Does the U.S. exercise contagion on Italy? a theoretical model and empirical evidence. Physica A: Statistical Mechanics and its Applications, 499, 436-442.
Fenga L (2018). Smoothing parameter estimation for first order discrete time infinite impulse response filters. Biometrics & Biostatistics International Journal, 7(5).
Fenga L (2017). A wavelet threshold denoising procedure for multimodel predictions: an application to economic time series. Statistical Analysis and Data Mining: the ASA Data Science Journal, 10(6), 410-421. Abstract.
Fenga L (2017). Bootstrap Order Determination for ARMA Models: a Comparison between Different Model Selection Criteria. Journal of Probability and Statistics, 2017, 1-12. Abstract.
Fenga L, Politis DN (2017). LASSO order selection for sparse autoregression: a bootstrap approach. Journal of Statistical Computation and Simulation, 87(14), 2668-2688.
Fenga L (2016). Time Series Chaos Detection and Assessment via Scale Dependent Lyapunov Exponent. International Journal of Statistics and Probability, 5(6), 1-1. Abstract.
Fenga L, Politis DN (2013). Bootstrap order selection for SETAR models. Journal of Statistical Computation and Simulation, 85(2), 235-250.
Fenga L, Politis DN (2011). Bootstrap-based ARMA order selection. Journal of Statistical Computation and Simulation, 81(7), 799-814.
Battaglia F, Fenga L (2003). Forecasting Composite Indicators with Anticipated Information: an Application to the Industrial Production Index. Journal of the Royal Statistical Society Series C: Applied Statistics, 52(3), 279-290. Abstract.
Missori P, Fenga L, Maraglino C, Rocchi G, Nardacci B, Calderaro G, Salvati M, Delfini R (2000). Spontaneous Acute Subdural Hematomas. A Clinical Comparison with Traumatic Acute Subdural Hematomas. Acta Neurochirurgica, 142(6), 697-701.
Chapters
Fenga L (2023). Publisher Correction to: Forecasting Combination of Hierarchical Time Series: a Novel Method with an Application to CoVid-19. In (Ed) Studies in Theoretical and Applied Statistics, Springer Nature, c1-c1.
Fenga L (2022). Forecasting combination of hierarchical time series: a novel method with an application to CoVid-19. In (Ed) Studies in Theoretical and Applied Statistics,, Springer. Abstract.
Quattrociocchi L, Tibaldi M, Caputi M (2020). Invecchiamento attivo e condizioni di vita degli anziani in Italia. In (Ed) .
Fenga L (2020). Multiscale Decomposition of Big Data Time Series for
Analysis and Prediction of Macroeconomic Data: a Recent Approach. In (Ed) Theory and Applications of Mathematical Science Vol. 3, BP International. Abstract.
Fenga L (2017). Prediction of Noisy ARIMA Time Series via Butterworth Digital Filter. In (Ed) Advances in Time Series Analysis and Forecasting Selected Contributions from ITISE 2016, Springer. Abstract.
Fenga L (2016). A Compounded Multiresolution Artificial Neural Network Method for the Prediction of Time
Series with Complex Dynamics. In (Ed) Time Series Analysis and Forecasting Selected Contributions from the ITISE Conference, Springer, 367-384. Abstract.
External Engagement and Impact
Invited lectures
Member of the Italian National Committee "Covid-Alert"
Invited lectures:
- Forecasting combination of hierarchical time series: a novel method with an application to CoVid-19 (solicited lecture)
- Forecasting of COVID–19 diffusion in Italy (European School of Onchology, May 13, 2020)
Teaching
I make a concerted effort to stay current in the areas of research and teaching methodology and I consistently look for ways to improve the learning process.My primary goal in teaching is to help students gain confidence and proficiency in learning so they acquire the foundation for lifelong learning and for professional pursuits.
Current module: BEM2039 - Business Analytics in Practice
