A wavelet-based multiscale vector-ANN model to predict comovement of econophysical systems

Saâdaoui, Foued; Rabbouch, Hana

doi:10.1016/j.eswa.2014.03.030

Cited by 34 publications

(20 citation statements)

References 13 publications

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“…As a matter of fact, the joint dynamics of the power prices and trading volume has been rarely considered in the literature, unlike those relating prices to other important variables such as loads, demand, weather, etc. [8][9][10][11] What is more, the method presented in this paper differs from previous research efforts by the fact that it proposes an effective 3-stage methodology aiming to model the covariation price-volume. The first stage is devoted to recognizing and extracting nonlinear patterns, via signal processing tools, before extrapolating them until a predefined time horizon.…”

Section: Introductionmentioning

confidence: 94%

“…Voronin et al have designed an ANN‐based hybrid methodology, which has the ability to predict the occurrence of normal range prices and price spikes in the electricity market. Other strategies have used preprocessing techniques to extract the smooth parts of the stochastic processes. Once extracted and modelled, smooth parts are exactly extrapolated on several temporal horizons before adding their corresponding forecast values.…”

Section: Previous Researchmentioning

confidence: 99%

“…Obersteiner and Saguan have given a simplistic representation of the wind/power‐price interaction in the Central European power market (Austria, Czech Republic, France, Germany, and Switzerland), where they have investigated the sensitivity of the market value on a number of wind power and system related parameters. Saâdaoui and Rabbouch have tested a wavelet‐based multivariate ANN allowing to simultaneously forecast electricity prices and demand in the Australian market. Maciejowska and Weron studied whether considering the fine structure of half hourly electricity prices, the market closing prices of fundamentals (natural gas, coal, and CO 2 ), and the system‐wide demand can lead to significantly more accurate forecasts of the APX UK baseload prices.…”

Section: Previous Researchmentioning

confidence: 99%

“…After having empirically analyzed the couple of variables and revealed the existence of a probable relationship between daily prices and their related traded volumes, a parametric modelling task was performed. As mentioned in Section 2, in the power markets' literature, 2 approaches are considered: parametric models essentially given by Misiorek et al and nonparametric methods such as the ANN and their extensions, for example, Wang and Ramsay, Conejo et al, and Saâdaoui and Rabbouch . The overwhelming part of studies have focused on single‐factor models.…”

Section: Empirical Modellingmentioning

confidence: 99%

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Stochastic modelling of the price‐volume relationship in electricity markets: Evidence from the Nordic energy exchange

Saâdaoui

Mrad

2017

Int Trans Electr Energ Syst

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Summary The analysis of the interactions between price and transaction volume, conducted as part of a financial market, is one of the central themes of the financial theory. However, with the emergence of power exchanges and despite the expansion of the research sphere in this context, this issue had not been sufficiently addressed. This is due to not only the lack of concepts in theory but also to the peculiarity of price dynamics. The purpose of this paper is to shed light on the nature of the relationship between price changes and trading volume in the Nordic power market (Nord Pool), which is one of the largest energy markets worldwide. Through statistical timing analysis of the price‐volume covariation, we reconsider this joint modelling, but only settling for daily observations, which makes the specificity of this work. Contrary to most comparable works, which were based on high frequency data, we chose to omit intraday observations to circumvent the repercussion of the daily seasonality on one side and ensure more lucidity and clairvoyance to a wider horizon on the other. The empirical results go in line with our expectations and provide support for the claim that there exists a significant reciprocal causality between prices and trading volume.

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Section: Introductionmentioning

confidence: 94%

Section: Previous Researchmentioning

confidence: 99%

Section: Previous Researchmentioning

confidence: 99%

Section: Empirical Modellingmentioning

confidence: 99%

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Stochastic modelling of the price‐volume relationship in electricity markets: Evidence from the Nordic energy exchange

Saâdaoui

Mrad

2017

Int Trans Electr Energ Syst

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“…A limitation of the method however is that it is data dependent. Saâdaoui and Rabbouch (2014) propose a wavelet based feed forward ANN. The model is nonlinear vector-autoregressive model.…”

Section: Time Series Analysismentioning

confidence: 99%

A unified model for context-based behavioural modelling and classification

Dabrowski

Villiers

2015

Expert Systems with Applications

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A unified Bayesian model that simultaneously performs behavioural modelling, information fusion and classification is presented. The model is expressed in the form of a dynamic Bayesian network (DBN). Behavioural modelling is performed by tracking the continuous dynamics of a entity and incorporating various contextual elements that influence behaviour. The entity is classified according to its behaviour. Classification is expressed as a conditional probability of the entity class given its tracked trajectory and the contextual elements. Inference in the DBN is performed using a derived Gaussian sum filter. The model is applied to classify vessels, according to their behaviour, in a maritime piracy situation. The novel aspects of this work include the unified approach to behaviour modelling and classification, the way in which contextual information is fused, the unique approach to classification according to behaviour and the associated derived Gaussian sum filter inference algorithm.

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Structured multifractal scaling of the principal cryptocurrencies: Examination using a self‐explainable machine learning

Saâdaoui,

Rabbouch

2024

Journal of Forecasting

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This paper introduces a novel statistical testing technique known as segmented detrended multifractal fluctuation analysis (SMF‐DFA) to analyze the structured scaling properties of financial returns and predict the long‐term memory of financial markets. The proposed methodology is applied to assess the efficiency of major cryptocurrencies, expanding upon conventional approaches by incorporating different fluctuation regimes identified through a change‐point detection test. A single‐factor model is employed to characterize the endogenous factors influencing scaling behavior, leading to the development of a self‐explanatory machine learning approach for price forecasting. The proposed method is evaluated using daily data from three major cryptocurrencies spanning from April 2017 to December 2022. The analysis aims to determine whether the digital market has experienced significant changes in recent years and assess whether this has resulted in structured multifractal behavior. The study identifies common periods of local scaling among the three prices, with a noticeable decrease in multifractality observed after 2018. Furthermore, complementary tests on shuffled and surrogate data are conducted to explore the distribution, linear correlation, and nonlinear structure, shedding light on the explanation of structured multifractality to some extent. Additionally, prediction experiments based on neural networks fed with multi‐fractionally differentiated data demonstrate the utility of this new self‐explanatory algorithm for decision‐makers and investors seeking more accurate and interpretable forecasts.

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A wavelet-based multiscale vector-ANN model to predict comovement of econophysical systems

Cited by 34 publications

References 13 publications

Stochastic modelling of the price‐volume relationship in electricity markets: Evidence from the Nordic energy exchange

Stochastic modelling of the price‐volume relationship in electricity markets: Evidence from the Nordic energy exchange

A unified model for context-based behavioural modelling and classification

Structured multifractal scaling of the principal cryptocurrencies: Examination using a self‐explainable machine learning

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