Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series

Millán, Ginno; Osorio-Comparán, Román; Lefranc, Gastón

doi:10.1109/icaacca51523.2021.9465274

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…A low standard deviation indicates that data is clustered around the mean, whereas a high standard deviation indicates that data is more dispersed. A standard deviation close to zero implies that the data sets are close to the mean, while a low or high standard deviation indicates that the sets of values are above or below the mean respectively [10] . The mathematical formula for calculating it is as follows:…”

Section: Data Usedmentioning

confidence: 99%

Analysis and Forecasting of Solar Power Generation using Machine Learning

Andrianajaiana,

DavidRazafimahefa,

Cristian-Gyozo

et al. 2023

IFR

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Current photovoltaic systems are equipped with a monitoring system. The data is recorded with a predefined time base. The operator has a large amount of data. This article is a contribution to the analysis and understanding of this data. It proposes to estimate Generation from this data using machine learning. The study was conducted at the 1Mw generation site in Miroslava, Iasi, Romania. The visualization and interpretation of the generation data is presented in this article. Then, prediction techniques are presented to obtain an estimate of the plant’s generation. These techniques are: Simple Exponential Smoothing (SES), Autoregression (AR), Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMA), Holt Winter’s Exponential Smoothing (HWES), Long Short Term Memory neural (LSTM) and Convolutional Neural Network (CNN).

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Section: Data Usedmentioning

confidence: 99%

Analysis and Forecasting of Solar Power Generation using Machine Learning

Andrianajaiana,

DavidRazafimahefa,

Cristian-Gyozo

et al. 2023

IFR

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The associative multifractal process: a new model for computer network traffic flows

Millán¹,

Osorio-Comparán²,

Lomas-Barrié³

et al. 2021

2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

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On the time series length for an accurate fractal analysis in network systems

Millán¹

2021

Preprint

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It is well-known that fractal signals appear in many fields of science. LAN and WWW traces, wireless traffic, VBR resources, etc. are among the ones with this behavior in computer networks traffic flows. An important question in these applications is how long a measured trace should be to obtain reliable estimates of de Hurst index (H). This paper addresses this question by first providing a thorough study of estimator for short series based on the behavior of bias, standard deviation ( ), Root-Mean-Square Error (RMSE), and convergence when using Gaussian H-Self-Similar with Stationary Increments signals (H-sssi signals). Results show that Whittle-type estimators behave the best when estimating H for short signals. Based on the results, empirically derived the minimum trace length for the estimators is proposed. Finally for testing the results, the application of estimators to real traces is accomplished. Immediate applications from this can be found in the real-time estimation of H which is useful in agent-based control of Quality of Service (QoS) parameters in the high-speed computer network traffic flows.

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Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series

Cited by 5 publications

References 26 publications

Analysis and Forecasting of Solar Power Generation using Machine Learning

Analysis and Forecasting of Solar Power Generation using Machine Learning

The associative multifractal process: a new model for computer network traffic flows

On the time series length for an accurate fractal analysis in network systems

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