A bibliography of neural network business applications research: 1994–1998

Wong, Bo K.; Lai, Vincent S.; Lam, Jolie

doi:10.1016/s0305-0548(99)00142-2

Cited by 125 publications

(63 citation statements)

References 275 publications

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“…Wong, Lai & Lam (2000) found over 300 research papers published on neural nets during 1994-1998. Early reviews on the accuracy of neural nets were not favourable; Chatfield (1993) refers to some of these.…”

Section: Neural Netsmentioning

confidence: 99%

Findings from evidence-based forecasting: Methods for reducing forecast error

Armstrong

2006

International Journal of Forecasting

151

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Empirical comparisons of reasonable approaches provide evidence on the best forecasting procedures to use under given conditions. Based on this evidence, I summarize the progress made over the past quarter century with respect to methods for reducing forecasting error. Seven well-established methods have been shown to improve accuracy: combining forecasts and Delphi help for all types of data; causal modeling, judgmental bootstrapping and structured judgment help with cross-sectional data; and causal models and trend-damping help with time-series data. Promising methods for cross-sectional data include damped causality, simulated interaction, structured analogies, and judgmental decomposition; for time-series data, they include segmentation, rule-based forecasting, damped seasonality, decomposition by causal forces, damped trend with analogous data, and damped seasonality. The testing of multiple hypotheses has also revealed methods where gains are limited: these include data mining, neural nets, and Box-Jenkins methods. Multiple hypotheses tests should be conducted on widely used but relatively untested methods such as prediction markets, conjoint analysis, diffusion models, and game theory. KeywordsBox-Jenkins, causal forces, causal models, combining forecasts, complex series, conjoint analysis, contrary series, damped seasonality, damped trend, data mining, Delphi, diffusion, game theory, judgmental decomposition, multiple hypotheses, neural nets, prediction markets, rule-based forecasting, segmentation, simulated interaction, structured analogies AbstractEmpirical comparisons of reasonable approaches provide evidence on the best forecasting

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Section: Neural Netsmentioning

confidence: 99%

Findings from evidence-based forecasting: Methods for reducing forecast error

Armstrong

2006

International Journal of Forecasting

151

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“…Y t = (S t -IMF 1t ) separately. As both the parts of the data are non-linear, they can be effectively modeled using ANN technique (Wong et al 2000). Eisner and Tsanis (1992) have proved that ANN works for modeling and extending the chaotic trajectories of the Lorenz equation.…”

Section: Fig 12mentioning

confidence: 99%

Regional level forecasting of seismic energy release

Kavitha

Raghukanth

2015

Acta Geod Geophys

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This article explores a new strategy for forecasting of earthquake energy release in the seismogenic zones of the world. A total of 41 active seismogenic zones are identified with the help of past seismicity data. The magnitudes of individual events occurred in each zone are converted into seismic energy using an empirical relation. The annual earthquake energy time series is constructed by adding the energy releases of all the events in a particular year. The technique of principal component analysis is employed for the regionalization of these seismogenic zones using seismic energy time series. The annual energy time series of seismogenic zones are decomposed into finite number of intrinsic mode functions (IMFs) using ensemble empirical mode decomposition technique. The periodicities of the IMFs and their contribution to the total variance of the earthquake energy release are examined. The artificial neural network technique is used for modeling and forecasting the energy-time series of seismogenic zones. The model is verified with an independent subset of data and validated using statistical parameters. The forecast of the annual earthquake energy release in each seismogenic zone is provided for the year 2015.

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“…According to Wong, Bodnovich, and Selvi (1995), the most frequent areas of ANN applications in the past 10 years are production/operations (53.5%) and finance (25.4%).…”

Section: Annsmentioning

confidence: 99%

Improved Crude Oil Price Forecasting With Statistical Learning Methods

Slim¹

2015

JMAA

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Reliable forecasts of the price of oil are of interest for a wide range of applications. For example, central banks and private sector forecasters view the price of oil as one of the key variables in generating macroeconomic projections and in assessing macroeconomic risks. Of particular interest is the question of the extent to which the price of oil is helpful in predicting recessions. This paper presents a statistical learning method (SLM) based on combined fuzzy system (FS), artificial neural network (ANN), and support vector regression (SVR) to cope with optimum long-term oil price forecasting in noisy, uncertain, and complex environments. A number of quantitative factors were discovered from this model and used as the input. For verification and testing, the West Texas Intermediate (WTI) crude oil spot price is used to test the effectiveness of the proposed learning methodology.Empirical results reveal that the proposed SLM-based forecasting can model the nonlinear relationship between the input variables and price very well. Furthermore, in-sample and out-of-sample prediction performance also demonstrates that the proposed SLM model can produce more accurate prediction results than other nonlinear models.Keywords: crude oil price, fuzzy system (FS), artificial neural networks (ANNs), support vector regression (SVR) IntroductionCrude oil has been playing an increasingly important role in the world economy since nearly two thirds of the world's energy demands are met from crude oil (Alvarez-Ramirez, Soriano, Cisneros, & Suarez, 2003). It is said that crude oil is also the world's largest and most actively traded commodity, accounting for over 10% of total world trade (Verleger, 1993). Like most commodities, crude oil price is mainly influenced by international politics, economy, military affairs, diplomacy, and other factors. The frequent change of these factors makes oil price show uncertainty, mutagenicity, and randomness. In addition, crude oil products are one of the world's major commodities with a high volatility level. They are traded in New York Mercantile Exchange (NYMEX) market together with other energy and mineral commodities. Simultaneously, the volatility of this crude oil price depends on demand and supply of the commodity, level of inventories, and economic indicators.In the past decades, traditional statistical and econometric techniques, such as linear regression (LinR), co-integration analysis, Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, naive (2001) suggested a semi-parametric statistical method for short-term oil price forecasting based on the GARCH properties of crude oil price. Similarly, Barone-Adesi, Bourgoin, and Giannopoulos (1986) suggested a semi-parametric approach for oil price forecasting. Gulen (1998) used a co-integration analysis to predict the West Texas Intermediate (WTI) price.Ye, Zyren, and Shore (2006) presented a simple econometric model of WTI prices, using OECD petroleum inventory levels, relative inventories, and high-and low-invento...

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A bibliography of neural network business applications research: 1994–1998

Cited by 125 publications

References 275 publications

Findings from evidence-based forecasting: Methods for reducing forecast error

Findings from evidence-based forecasting: Methods for reducing forecast error

Regional level forecasting of seismic energy release

Improved Crude Oil Price Forecasting With Statistical Learning Methods

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