Holt’s exponential smoothing and neural network models for forecasting interval-valued time series

Maia, André Luis Santiago; Carvalho, Francisco de A. T. de

doi:10.1016/j.ijforecast.2010.02.012

Cited by 141 publications

(71 citation statements)

References 42 publications

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“…To justify whether considering the influencing variables is useful for achieving better prediction performance, a MSVR-PSO model without the influencing variables (MSVR-PSO-WO) is also chosen as a benchmark. Detailed formulations of these selected methods (Holt I , VECM, and ANN) can be found in [30,31].…”

Section: Statistical Criteria and Methodologies Implementationmentioning

confidence: 99%

“…The Holt I is implemented with the methods used by Maia and de Carvalho [30]. We estimate the smoothing parameter matrices with elements constrained to the rang (0, 1), by minimizing the interval sum of the squared forecasting errors.…”

Section: Statistical Criteria and Methodologies Implementationmentioning

confidence: 99%

“…This is a classic accuracy measure and has been widely adopted for interval forecasting [22,30,35]. The definition of I is as follows:…”

Section: Pso For Parameter Selection Of Msvr Particle Swarm Optimizamentioning

confidence: 99%

“…In addition, two traditional ITS modeling techniques, Holt's exponential smoothing method for intervals (Holt I ) [30] and vector error correction model (VECM) [31], are also selected as benchmarks. It should be noted that, in the cases of Holt I and VECM, only historical lower and upper bounds of carbon futures prices (without influencing variables) are used as predictors.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables

Zhang

Xiong

et al. 2017

Discrete Dynamics in Nature and Society

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show abstract

Section: Statistical Criteria and Methodologies Implementationmentioning

confidence: 99%

Section: Statistical Criteria and Methodologies Implementationmentioning

confidence: 99%

“…This is a classic accuracy measure and has been widely adopted for interval forecasting [22,30,35]. The definition of I is as follows:…”

Section: Pso For Parameter Selection Of Msvr Particle Swarm Optimizamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables

Zhang

Xiong

et al. 2017

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

show abstract

“…There are also some forecasting models built on the binary interval number directly. Maia et al [7,8] provided the forecasting method of the binary interval number series based on the autoregressive integrated moving average (ARIMA) and the artificial neural network (ANN) model. Pellegrini et al [9] provided a forecasting method of the binary interval number series based on ARMA-GARCH model.…”

Section: Introductionmentioning

confidence: 99%

Triangular fuzzy series forecasting based on grey model and neural network

Zeng

Shu

Huang

et al. 2016

Applied Mathematical Modelling

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Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?

Fischer

Blanco-Fernández

Winker

2015

Journal of Forecasting

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We study the performance of recently developed linear regression models for interval data when it comes to forecasting the uncertainty surrounding future stock returns. These interval data models use easy-to-compute daily return intervals during the modeling, estimation and forecasting stage. They have to stand up to comparable point-data models of the well-known capital asset pricing model type-which employ single daily returns based on successive closing prices and might allow for GARCH effects-in a comprehensive out-of-sample forecasting competition. The latter comprises roughly 1000 daily observations on all 30 stocks that constitute the DAX, Germany's main stock index, for a period covering both the calm market phase before and the more turbulent times during the recent financial crisis. The interval data models clearly outperform simple random walk benchmarks as well as the point-data competitors in the great majority of cases. This result does not only hold when one-day-ahead forecasts of the conditional variance are considered, but is even more evident when the focus is on forecasting the width or the exact location of the next day's return interval. Regression models based on interval arithmetic thus prove to be a promising alternative to established point-data volatility forecasting tools.

show abstract

Holt’s exponential smoothing and neural network models for forecasting interval-valued time series

Cited by 141 publications

References 42 publications

Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables

Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables

Triangular fuzzy series forecasting based on grey model and neural network

Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?

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