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Forecasting and forecast-combining of quarterly earnings-per-share via genetic programming
(Universidad de Chile. Facultad de Economía y Negocios, 2008)
In this study we examine different methodologies to estimate
earnings. More specifically, we evaluate the viability of Genetic
Programming as both a forecasting model estimator and a forecastcombining
methodology. When ...
Reconfiguration and reinforcement allocation as applied to hourly medium-term load forecasting of distribution feeders
(Institution of Engineering and Technology, 2020)
In this study, a methodology to develop hourly demand scenarios in a medium-term horizon for primary distribution substations is presented and applied to a case study. The main contribution of this study is that it addresses ...
SVR-FFS: A novel forward feature selection approach for high-frequency time series forecasting using support vector regression
(Elsevier, 2020)
n this paper, we propose a novel support vector regression (SVR) approach for time series analysis. An efficient forward feature selection strategy has been designed for dealing with high-frequency time series with multiple ...
PM2.5 forecasting in Coyhaique, the most polluted city in the Americas
(Elsevier, 2020)
Coyhaique is a southern Chilean city with a population of approximately 64,000 habitants. In
spite of its small size, Coyhaique has been identified as the city with highest annual PM2.5
concentrations of the Americas ...
Empirical analysis of systematic errors in chilean GDP forecasts
(Wiley, 2001)
This paper presents a statistical comparison between the actual and predicted
evolution of the Chilean GDP for the period 0875Ð0887 made by several
forecasters[ We show that the forecasters systematically underestimate ...
Aggregation systems for sales forecasting
(Elsevier, 2015)
Sales forecasting consists of calculating the expected sales of a specific product or company. An important issue
when dealing with sales forecasting is the calculation of the average sales, usually using the arithmetic ...
A random walk through the trees: Forecasting copper prices using decision learning methods
(Elsevier, 2020)
We investigate the accuracy of copper price forecasts produced by three decision learning methods. Prior evidence (Liu et al. Resources Policy, 2017) shows that a regression tree, a simple decision learning model, can be ...
A time-series forecasting performance comparison for neural networks with state space and ARIMA models
(IEOM Society InternationalUnited States, 2020)
Fuzzy Prediction Interval Models for Forecasting Renewable Resources and Loads in Microgrids
(IEEE-Inst Electrical Electronics Engineers Inc, 2015)
Millennium Institute Complex Engineering Systems
ICM: P-05-004-F
CONICYT: FBO16
National Fund for Science and Technology
1140775
CONICYT/FONDAP/15110019
Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation
(Elsevier, 2021)
Demand forecasting and capacity management are complicated tasks for emergency healthcare services due to
the uncertainty, complex relationships, and high public exposure involved. Published research does not show
integrated ...