The delay vector variance method and the recurrence quantification analysis of energy markets
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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in: International Journal of Energy and Statistics, Jahrgang 4, Nr. 1, 1650001, 31.03.2016.
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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TY - JOUR
T1 - The delay vector variance method and the recurrence quantification analysis of energy markets
AU - Fianu, Emmanuel Senyo
PY - 2016/3/31
Y1 - 2016/3/31
N2 - Deregulation in the energy industry poses many challenges because energy plays a significant role in the economies of every nation. The identification of power spikes and the likelihood of power crisis is important to prevent the collapse of the energy sector that can severely affect economies. This paper employs the recently proposed “delay vector variance” method, which examines local predictability of a signal in the phase space to detect the presence of deterministic and non-linearity in time series. The DVV approach utilizes optimal embedding parameters that are obtained via a differential entropy based method using wavelet-based surrogates. The concept of (cross)-recurrence quantification analysis is used to study energy markets in order to locate hidden patterns, non-stationarity, potential spikes and examine the nature of these plots in the event of crisis in the energy and financial sector. Specifically, the recurrence plots are employed to detect and characterize seasonal cycles. The feasibility of these methods are provided with a focus on some emerging European power markets that are useful in the diagnosis and detection of potential power spikes, which is significantly impacted by economic downturns and other main drivers of imbalances in power markets.
AB - Deregulation in the energy industry poses many challenges because energy plays a significant role in the economies of every nation. The identification of power spikes and the likelihood of power crisis is important to prevent the collapse of the energy sector that can severely affect economies. This paper employs the recently proposed “delay vector variance” method, which examines local predictability of a signal in the phase space to detect the presence of deterministic and non-linearity in time series. The DVV approach utilizes optimal embedding parameters that are obtained via a differential entropy based method using wavelet-based surrogates. The concept of (cross)-recurrence quantification analysis is used to study energy markets in order to locate hidden patterns, non-stationarity, potential spikes and examine the nature of these plots in the event of crisis in the energy and financial sector. Specifically, the recurrence plots are employed to detect and characterize seasonal cycles. The feasibility of these methods are provided with a focus on some emerging European power markets that are useful in the diagnosis and detection of potential power spikes, which is significantly impacted by economic downturns and other main drivers of imbalances in power markets.
KW - Sustainability sciences, Management & Economics
KW - Delay vector variance method
KW - electricity spot prices
KW - embedding parameters
KW - nonlinear time series analysis
KW - spikes
KW - recurrence plots
U2 - 10.1142/S2335680416500010
DO - 10.1142/S2335680416500010
M3 - Journal articles
VL - 4
JO - International Journal of Energy and Statistics
JF - International Journal of Energy and Statistics
SN - 2335-6804
IS - 1
M1 - 1650001
ER -