Author: MAURICIO FRANCA LILA
Supervisors: Fernando Luiz Cyrino Oliveira & Erick Meira de Oliveira
Date and Time: 28/04/2023, 10:30h
Link / Room: https://puc-rio.zoom.us/j/92183949231?pwd=NllydW5pTDlLNFltN0pIN2FQa1JCdz09
Committee: Fernando Luiz Cyrino Oliveira – orientador – PUC-Rio; Erick Meira de Oliveira – co-orientador -FINEP; Helio Côrtes Vieira Lopes – PUC-Rio; Reinaldo Castro Souza – PUC-Rio; Lilian Manoel de Menezes Willenbockel – UL; Lupercio França Bessegato – UFJF; Paulo Jorge Canas Rodrigues – UFBA.
Abstract:
This study presents a set of methodological proposals related to the forecast reconciliation
in the context of Hierarchical Time Series. The main objective is to present original solutions to the theme, seeking to obtain more accurate forecasts than those obtained by independent models for the different levels of the hierarchy. The studies were conducted in real data, showing the potentiality of application of the methods developed in different scenarios, where the time series are structures in a hierarchical fashion. This thesis is composed of a set of essays that explore forecast reconciliation from the perspective of a regression model, which gives foundations to optimal reconciliation. The first contribution addresses the problem of forecast reconciliation from the perspective of robust estimators. The proposal presents an original contribution applied to data from labor force surveys in Brazil, presenting a set of solutions that can drive efficient public policies. In this case, the reconciled forecasts obtained through robust estimators provided consistent gains in terms of accuracy when compared to methods that represent the state-of-the-art on forecast reconciliation in hierarchical time series. The second contribution deals with the problem of optimal reconciliation applied to energy consumption series in Brazil. We presented an alternative proposal, less sensitive to outlying forecasts at the reconciliation stage. The results obtained in this second study show considerable improvements in standard evaluation metrics with regard to the new forecasts. A third proposal seeks to offer robust covariance structures of forecasting errors, which expands the set of strategies presented in the literature. The main contribution is to incorporate robust covariance estimates into the MinT (Minimum Trace) reconciliation approach, which minimizes reconciliation errors, offering an estimator with minimum variance.