A combination selection algorithm on forecasting

Cang, Shuang; Yu, Hongnian

doi:10.1016/j.ejor.2013.08.045

Cited by 65 publications

(32 citation statements)

References 71 publications

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“…To date ARIMA models are still considered the dominant benchmark in empirical forecasting evaluations, and find great popularity among OR researchers in applications spanning from hospitality and production to healthcare and climate forecasting (for e.g. see Broyles et al, 2010;Cao et al, 2012;Cang and Yu, 2014).…”

Section: Background Literaturementioning

confidence: 99%

‘Horses for Courses’ in demand forecasting

Petropoulos

Makridakis

Assimakopoulos

et al. 2014

European Journal of Operational Research

179

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Forecasting as a scientific discipline has progressed a lot in the last forty years, with Nobel prizes being awarded for seminar work in the field, most notably to Engle, Granger and Kahneman. Despite these advances, even today we are unable to answer a very simple question, the one that is always the first tabled during discussions with practitioners: "what is the best method for my data?". In essence, as there are horses for courses, there must also be forecasting methods that are more tailored to some types of data, and, therefore, enable practitioners to make informed method selection when facing new data. The current study attempts to shed light on this direction via identifying the main determinants of forecasting accuracy, through simulations and empirical investigations involving fourteen popular forecasting methods (and combinations of them), seven time series features (seasonality, trend, cycle, randomness, number of observations, inter-demand interval and coefficient of variation) and one strategic decision (the forecasting horizon). Our main findings dictate that forecasting accuracy is influenced as follows: a) for fast-moving data, cycle and randomness have the biggest (negative) effect and the longer the forecasting horizon, the more accuracy decreases; b) for intermittent data, inter-demand interval has bigger (negative) impact than the coefficient of variation; c) for all types of data, increasing the length of a series has a small positive effect.

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Section: Background Literaturementioning

confidence: 99%

‘Horses for Courses’ in demand forecasting

Petropoulos

Makridakis

Assimakopoulos

et al. 2014

European Journal of Operational Research

179

View full text Add to dashboard Cite

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“…Many authors have acknowledged the importance of applying new approaches to tourism demand forecasting in order to improve the accuracy of the methods of analysis (Song, Li 2008). The availability of more advanced forecasting techniques has led to a growing interest Artificial Intelligence (AI) models (Yu, Schwartz 2006;Goh et al 2008;Lin et al 2011;Chen 2011;Celotto et al 2012;Wu et al 2012;Cang, Yu 2014) to the detriment of time series models (Chu 2008(Chu , 2011Assaf et al 2011) and causal econometric models (Page et al 2012). Some of the new AI based techniques are fuzzy time series models (Tsaur, Kuo 2011), genetic algorithms (Hadavandi et al 2011), expert systems (Shahrabi et al 2013;Pai et al 2014) and Support Vector Machines (SVMs) (Chen, Wang 2007;Hong et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Data Pre-Processing for Neural Network-Based Forecasting: Does It Really Matter?

Clavería

Monte

Porras

2015

Technological and Economic Development of Economy

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Abstract.This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and Elman neural networks. The structure of the networks is based on a multiple-output approach. We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels.Keywords: artificial neural networks, forecasting, multiple-output, seasonality, detrending, tourism demand, multilayer perceptron, radial basis function, Elman JEL classification: L83, C53, C45, R11 IntroductionInternational tourism has become one of today's fastest growing industries. Tourism accounts for almost 10% of total international trade and plays a fundamental role in the long-run economic development of many regions (Akkemik 2012;Sigala, Chalkiti 2014). To achieve a sustainable tourism model, policy makers and professionals need more accurate predictions of the number of tourist arrivals at the destination level. Many authors have acknowledged the importance of applying new approaches to tourism demand forecasting in order to improve the accuracy of the methods of analysis (Song, Li 2008). The availability of more advanced forecasting techniques has led to a growing interest Artificial Intelligence (AI) models (Yu, Schwartz 2006;Goh et al. 2008;Lin et al. 2011;Chen 2011;Celotto et al. 2012;Wu et al. 2012;Cang, Yu 2014) to the detriment of time series models (Chu 2008(Chu , 2011Assaf et al. 2011) and causal econometric models (Page et al. 2012). Some of the new AI based techniques are fuzzy time series models (Tsaur, Kuo 2011), genetic algorithms (Hadavandi et al. 2011), expert systems (Shahrabi et al. 2013;Pai et al. 2014) and Support Vector Machines (SVMs) (Chen, Wang 2007;Hong et al. 2011). Recent research has shown the suitability of Artificial Neural Networks (ANNs) for dealing with tourism demand forecasting (Teixeira, Fernandes 2012;Claveria, Torra 2014).In spite of the successful use of ANNs for time series forecasting, very few studies compare the accuracy of different NN architectures for tourism demand forecasting at a regional level. The present study deals with tourist a...

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“…For this reason, making the appropriate maintenance (repair, renovation, replacement) only after the damage of the element may be irrational, control human intervention detects and removes faults being a potential source of failure. Widely used solution become the preventive renewals, aimed at reducing the loss of utility of a given element in different environment [4,8,21]. Very important from operator perspective is the failure reason [18,20,29], as well as modernizing actions that should be taken to avoid such undesirable situations.…”

mentioning

confidence: 99%

Analysis of the gas network failure and failure prediction using the Monte Carlo simulation method

Tchórzewska-Cieślak

Pietrucha-Urbanik

Urbanik

2016

EiN

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Article citation info: IntroductionIn the report of the Committee of Union Gas in the World Congress in Nice in 1972, the issue connected with the failure intensity of the gas network was presented. Operation of Gas Supply System is inseparably linked with the occurrence of the fire and explosion risk. Failures, explosions and fires long-term statistics conducted by the gas services and organizational units of the State Fire Service indicate that despite the continuous increase of the safety level of gas supply system and number of actions taken in this regard, still many failures are recorded, which often generate serious threat for the external and the inner environment of a man, as well as create risk for human health and life. The consequences of failures, during which the release and dispersion of natural gas occurs, depend on its type, nature and causes, as well as from the efficiency of its removal.Failure frequency evaluation in the operation of gas supply subsystem should be one of the priorities of the gasworks, which should attach greater importance to assess the proper functioning of the newly designed, as well as the implemented systems [31]. With the increasing pressure from the environmentalists and stricter standards for acceptable environmental pollution and structures vulnerability in terms of its protection, it seems to be a necessity [6,24,28]. The issue connected with failure gas pipeline analysis in perspective of improper design, construction and maintenance is presented in work [11], the increased failure affected by the lack or improper conduct of repairs and modernization, also incorrect or lack of risk management program. When planning renovations or modernization of gas networks the gasworks employees should make the classification of pipelines, that means determine which sections of the network require immediate repair, and which can be repaired later [7,15,26]. For this reason, making the appropriate maintenance (repair, renovation, replacement) only after the damage of the element may be irrational, control human intervention detects and removes faults being a potential source of failure. Widely used solution become the preventive renewals, aimed at reducing the loss of utility of a given element in different environment [4,8,21]. Very important from operator perspective is the failure reason [18,20,29], as well as modernizing actions that should be taken to avoid such undesirable situations. The strategy of the preventive renewals is to establish such timing of the renewals which will enable to achieve the maximum profitability of the project, through the use of the periodic strategies involving the renewals after a certain period of element operation [5,11]. Such classification can be made, for example, on the basis of failure prediction for certain sections of the gas network, using the Monte Carlo method [30,34]. The Monte Carlo methods include all proceedings aimed at finding approximate solutions of some problems (mathematical, technical or operational) [27]. The Monte Carlo ...

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A combination selection algorithm on forecasting

Cited by 65 publications

References 71 publications

‘Horses for Courses’ in demand forecasting

‘Horses for Courses’ in demand forecasting

Data Pre-Processing for Neural Network-Based Forecasting: Does It Really Matter?

Analysis of the gas network failure and failure prediction using the Monte Carlo simulation method

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