Spectral Analysis of Irregularly Sampled Data with Time Series Models

Broersen, P.M.T.

doi:10.2174/1876825300801010007

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Moreover, the Lomb-Scargle method was applied (Broersen 2008), which is a method for analyzing non-uniform data and fit sinusoidal functions to time series data. Data on the clearness index under clear sky conditions, K To , for spectral analysis …”

Section: Modeling Solar Radiationmentioning

confidence: 99%

Spectral Analysis of Hourly Solar Radiation

Ambas

Baltas

2014

Environ. Process.

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Τhis research aims at the precise estimation of solar radiation, on an hourly and daily basis, by using measured data of solar radiation and sunshine duration. Data is collected from the automatic meteorological station of Amynteo, West Macedonia, Greece. For the correlation of solar radiation and sunshine duration, the Angström-Prescott equation is selected. Two limit conditions of cloud coverage are examined, overcast and clear sky, for the estimation of the factors a s and b s of the Angström-Prescott equation. The factor a s is the minimum transmissivity for overcast sky, while a s +b s is expressing the clear sky transmissivity. In addition, spectral analysis is performed to investigate the periodicities of the factors a s and b s . The Lomb-Scargle periodogram is applied, as data are unevenly distributed in time, and reveals the existence of two periodicities for the b s factor. More specifically, annual periodicity with an estimated width equal to 0.032, and daily periodicity with a width equal to 0.184. As a result of this research, a sinusoidal equation with two harmonics is developed to calculate the b s factor, which coincides with the clearness index (R s /R a ) under clear sky. In overcast conditions, the clearness index does not exhibit periodicities. The derived equation is used to produce estimations of solar radiation values on an hourly and daily basis. The daily values estimated from the new equation gives better results when compared to typical regression analysis.

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Section: Modeling Solar Radiationmentioning

confidence: 99%

Spectral Analysis of Hourly Solar Radiation

Ambas

Baltas

2014

Environ. Process.

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“…Existing methods for analyzing irregular time series can be categorized into three main directions [9]: (i) the repair approach in which missing observations are recovered via smoothing or imputation [10][11][12][13][14]-also implemented, especially in recent years, by machine learning methods [15][16][17][18]; (ii) the generalization of spectral analysis tools [19,20], such as wavelets [21][22][23][24]; (iii) kernel methods [25,26]. In this paper, we deal with a repair approach which uses an input preparation step based on machine learning.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Methods for Jump-Diffusion Models of Power Prices

Mari

Baldassari

2021

Energies

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We propose a machine learning-based methodology which makes use of ensemble methods with the aims (i) of treating missing data in time series with irregular observation times and detecting anomalies in the observed time behavior; (ii) of defining suitable models of the system dynamics. We applied this methodology to US wholesale electricity price time series that are characterized by missing data, high and stochastic volatility, jumps and pronounced spikes. For missing data, we provide a repair approach based on the missForest algorithm, an imputation algorithm which is completely agnostic about the data distribution. To identify anomalies, i.e., turbulent movements of power prices in which jumps and spikes are observed, we took into account the no-gap reconstructed electricity price time series, and then we detected anomalous regions using the isolation forest algorithm, an anomaly detection method that isolates anomalies instead of profiling normal data points as in the most common techniques. After removing anomalies, the additional gaps will be newly filled by the missForest imputation algorithm. In this way, a complete and clean time series describing the stable dynamics of power prices can be obtained. The decoupling between the stable motion and the turbulent motion allows us to define suitable jump-diffusion models of power prices and to provide an estimation procedure that uses the full information contained in both the stable and the turbulent dynamics.

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“…Για τη µέθοδο αυτή υπάρχουν έτοιµα προγράµµατα σε Fortran 77 (Press et al, 1992), Turbo Pascal (Schullz andStattegger, 1998), MATLAB (Brett Shoelson, 2003). Σηµαντικές εργασίες µε εφαρµογή της µεθόδου Lomb έχουν παρουσιάσει οι De Waele ( 2003), Gentine (2006), Dilmaghani (2007), Trauth (2007), Broersen (2008.…”

Section: φασµατική ανάλυση δεδοµένων σε άνισα χρονικά διαστήµαταunclassified

Διερεύνηση Και Εκτίμηση Μετεωρολογικών Παραμέτρων Άμεσης Επίδρασης Στη Γεωργία

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Spectral Analysis of Irregularly Sampled Data with Time Series Models

Cited by 5 publications

References 19 publications

Spectral Analysis of Hourly Solar Radiation

Spectral Analysis of Hourly Solar Radiation

Ensemble Methods for Jump-Diffusion Models of Power Prices

Διερεύνηση Και Εκτίμηση Μετεωρολογικών Παραμέτρων Άμεσης Επίδρασης Στη Γεωργία

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