Recognizing changing seasonal patterns using artificial neural networks☆
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2023, International Journal of ForecastingCitation Excerpt :In technical analysis applications, Hans and van Griensven Kasper (1998), Gençay and Stengos (1998), and Fernández-Rodríguez et al. (2000) indicated that utilizing past buy–sell signals of foreign exchange rates and stock prices in a feedforward ANN improves profitability and out-of-sample generalizations. Franses and Draisma (1997) proposed a method based on ANNs to investigate when and how seasonal patterns in macroeconomic time series change over time. Yang et al. (1999) used probabilistic ANNs in bankruptcy prediction.
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2022, Resources PolicyCitation Excerpt :Thus, there are important economic mechanisms between them, which is the basis of forecasting COPV. Second and more important, significantly seasonal behavior has been observed in these macro variables (Sealey, 1977; Franses and Draisma, 1997; Tiwari et al., 2014). Hence, whether and how this important characteristic of the macro variables can be employed in modeling the forecasting of COPV?
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2020, Applied EnergyCitation Excerpt :The study by [23] used an ANN model to forecast electric load. The popularity of ANN models has been increasing because they have ability to model a time series data with seasonal patterns without any pre-processing[24–27]. Moreover, ANN models not only can deal with complex patterns, but they also perform well with missing and incomplete data [28].
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2019, Annals of Tourism ResearchCitation Excerpt :However, it is noteworthy that there have been some authors who reported that NN can model the trend and seasonal effects in time series without the need for denseasonaling (Hamzacebi, 2008). See for example, Franses and Draisma (1997) and Alon, Qi, and Sadowski (2001). Tseng, Yu, and Tzeng (2002) sought to create a hybrid model which combined NN back propagation (BP) with a seasonal ARIMA (SARIMA) model and found this hybrid model could outperform two other NN models and the univariate SARIMA model when faced with seasonal time series.
Data driven model for sonic well log prediction
2018, Journal of Petroleum Science and EngineeringCitation Excerpt :Most importantly, there is no set rule on how an ANN derived model should be designed or developed to get the best results (Adedigba et al., 2017). For this reason, it is recommended to adopt simple ANN architecture except in cases where complex correlations will better describe the existing structure between the input and target values (Franses and Draisma, 1997). Note that increasing the number of input vectors or data is not synonymous with a better prediction as it is possible to over fit the data.
Artificial neural networks in business: Two decades of research
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This paper was presented at an Institute of Economics seminar in Aarhus (November 1994) and at the (EC)2 conference in Berlin (December 1994).
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We thank the participants for their helpful comments. Special thanks go Herman Bierens, Svend Hylleberg, Johan Kaashoek, Halbert White, and to Helmut Lütkepohl (the Associate Editor) and two anonymous referees for their helpful suggestions.
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The first author thanks the Royal Netherlands Academy of Arts and Sciences for its financial support.