Use of machine learning and multilevel analysis in hierarchical approaches of public expenditure forecasting

On the one hand, public expenditure can be decomposed into several levels of expenditure regarding each administrative unit and expenditure nature groups that happens thru time. Therefore, it can be considered as a hierarchical time series. On the other hand, accurate forecasting methods are desirab...

Full description

Saved in:
Bibliographic Details
Main Authors: Neto, Jose Ivo Carille, Neto, Fernando B. de L., Joao de Oliveira, F. L.
Format: Conference Proceeding
Language:English
Subjects:
Coherence
extreme learning machines
Forecasting
Hierarchical time series
Machine learning
Planning
Predictive models
public expenditure
Support vector machines
support vector regression
Time series analysis
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:On the one hand, public expenditure can be decomposed into several levels of expenditure regarding each administrative unit and expenditure nature groups that happens thru time. Therefore, it can be considered as a hierarchical time series. On the other hand, accurate forecasting methods are desirable for planning and management due to the need to identify and anticipate future scenarios. Thus, considering the hierarchical nature of the problem, this work aims to develop a a predictive model which takes into consideration the hierarchical structure of public expenditure in order to assure financial coherence and achieve improved results. Experimentally, this work uses time series of public expenditure execution in the State of Pernambuco, Brazil. The experiments were conducted on two axes in order to establish a multilevel analysis considering conciliatory approaches of the hierarchical levels of expenditure and the predictive models. By applying these methods, valuable public expenditure forecasts in the State of Pernambuco considering different hierarchical levels were produced.
ISSN:2577-1655
DOI:10.1109/SMC53654.2022.9945361