STAR and ANN models: forecasting performance on the Spanish “Ibex-35” stock index

Pérez‐Rodríguez, Jorge V.; Porras, Salvador Torra; Félix, Julián Andrada

doi:10.1016/j.jempfin.2004.03.001

Cited by 59 publications

(20 citation statements)

References 37 publications

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“…The one-step forecasting (examined here) is useful in single applications, e.g., in assessing forecasting methods using simulations [8], engineering forecasting [9,10], environmental forecasting [11,12], and financial forecasting [13][14][15][16][17]. Furthermore, the recursive and the DiRec multi-step forecasting strategies depend on the one-step forecasting.…”

Section: Time Series Forecasting and Random Forestsmentioning

confidence: 99%

Variable Selection in Time Series Forecasting Using Random Forests

2017

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Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

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Section: Time Series Forecasting and Random Forestsmentioning

confidence: 99%

Variable Selection in Time Series Forecasting Using Random Forests

2017

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“…Seasonally adjusted series are obtained using a Census X12 filter with a multiplicative decomposition. Forecasts for 1, 3 and 6 months ahead are computed in a recursive way (Pérez-Rodríguez et al 2005). To summarize this information we have computed the Mean Absolute Percentage Error (MAPE) statistic for forecast accuracy.…”

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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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“…Regarding measurable criteria, the outcomes demonstrate that distinctive fake neural system particulars gauge superior to the AR model and smooth move non-straight models [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Stock price forecasting model analysis based on the wavelet neural network and the data cleaning technology

Sun¹,

Tang²,

Guo³

2016

Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications

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Abstract. Financial time arrangement, for example, stock cost and trade rates are, regularly, non-straight and non-stationary. Beforehand, numerous scientists have endeavored to conjecture those utilizing factual models and machine learning models. Measurable models expect the time arrangement to be stationary and straight, accordingly bringing about expansive factual mistakes. Examination and expectation of securities exchange time arrangement information has pulled in impressive enthusiasm from the exploration group in the course of the most recent decade. Quick advancement and development of modern calculations for measurable examination of time arrangement information, and accessibility of elite equipment has made it conceivable to prepare and dissect high volume securities exchange time arrangement information successfully, continuously. In monetary field, exceptions speak to unpredictability of securities exchange, which assumes an imperative part in administration, portfolio choice and subordinate evaluating. Along these lines, anticipating anomalies of securities exchange is of the immense significance in principle and application. In this paper, the issue of anticipating anomalies in light of versatile gathering models of Intense Erudition Machines (IEMs) is considered. We discovered that the prescribed novel model is material for exception estimating and beats the strategies in light of auto relapse (AR) Intense Erudition Machines (IEMs) models.

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STAR and ANN models: forecasting performance on the Spanish “Ibex-35” stock index

Cited by 59 publications

References 37 publications

Variable Selection in Time Series Forecasting Using Random Forests

Variable Selection in Time Series Forecasting Using Random Forests

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

Stock price forecasting model analysis based on the wavelet neural network and the data cleaning technology

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