Parcourir par sujet "Time series"
Voici les éléments 1-19 de 19
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Application of Fuzzy Cognitive Maps to water demand prediction
(2015)This article is focused on the issue of learning of Fuzzy Cognitive Maps designed to model and predict time series. The multi-step supervised-learning based-on-gradient methods as well as population-based learning, with ... -
Applying Long Short-Term Memory Networks for natural gas demand prediction
(2019)Long Short-Term Memory (LSTM) algorithm encloses the characteristics of the advanced recurrent neural network methods and is used in this research study to forecast the natural gas demand in Greece in the short-term. LSTM ... -
Backward Degree a new index for online and offline change point detection based on complex network analysis
(2022)How to identify an upcoming transition in a time series continues to be an important open research issue. In various fields of physical sciences, engineering, finance and neuroscience abrupt changes can occur unexpectedly ... -
Collaborative migration, coupling and simulation of water resources models through OpenMI
(2010)The present study describes the migration and coupling of a monthly conceptual rainfall runoff model named UTHBAL and a water reservoir operation model called UTHRL into the OpenMI standard. The models have been introduced ... -
Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis
(2018)Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly ... -
Exploring an ensemble of methods that combines fuzzy cognitive maps and neural networks in solving the time series prediction problem of gas consumption in Greece
(2019)This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim ... -
Forecasting urban expansion based on night lights
(2016)Forecasting urban expansion models are a very powerful tool in the hands of urban planners in order to anticipate and mitigate future urbanization pressures. In this paper, a linear regression forecasting urban expansion ... -
A hybrid downscaling approach for the estimation of climate change effects on droughts using a geo-information tool. Case study: Thessaly, Central Greece
(2016)Multiple linear regression is used to downscale large-scale outputs from CGCM2 (second generation CGCM of Canadian centre for climate monitoring and analysis) and ECHAM5 (developed at the Max Planck Institute for Meteorology), ... -
Hybrid model for water demand prediction based on fuzzy cognitive maps and artificial neural networks
(2016)In this study, we propose a new hybrid approach for time series prediction based on the efficient capabilities of fuzzy cognitive maps (FCMs) with structure optimization algorithms and artificial neural networks (ANNs). ... -
Hybrid Time-series Representation for the Classification of Driving Behaviour
(2020)The classification of driving behaviour is important for monitoring driving risk and fuel efficiency, as well as for providing a personalized view, or 'fingertip', of each driver, useful in driving assistance and car ... -
Hydrologic processes simulation using the conceptual model Zygos: the example of Xynias drained Lake catchment (central Greece)
(2016)In the catchment of Xynias drained Lake, hydrologic processes simulation took place using a lumped approach with the conceptual model Zygos. The model implements a conceptual soil moisture accounting scheme extended with ... -
Machine learning technique in time series prediction of gross domestic product
(2017)Artificial intelligence is gaining ground the last years in many scientific sectors with the development of new machine learning techniques. In this research, a machine learning methodology is proposed in the Gross Domestic ... -
Recurrence quantification analysis of MHD turbulent channel flow
(2019)We present Recurrence Plots (RPs) and Recurrence Quantification Analysis (RQA) of time series of velocities in a low-Reynolds-number magnetohydrodynamic turbulent channel flow. The flow was simulated using a fully spectral ... -
RNNs for Classification of Driving Behaviour
(2019)Recurrent neural networks are an obvious choice for driving behavior analysis by means of time series of measurements, obtained either from telematics or mobile phone sensors. This work investigates such an application, ... -
Spatiotemporal Analysis of Seawatch Buoy Meteorological Observations
(2015)In the present study, we analyzed meteorological observations from Seawatch buoys in the Mediterranean Sea and specifically from locations in the Aegean and Ionian Sea. The data were collected from buoys that have been ... -
Time series formation based on VIIRS 24h data
(2020)Satellite images taken in the night (a.k.a night lights) have been extensively used as a proxy for economic activity and urbanization. The recent Visible Infrared Imaging Radiometer Suite (VIIRS) data available every 24h ... -
Urban water demand forecasting for the Island of skiathos
(2014)We present an analysis of historical water demand data from the utility of Skiathos, Greece and demonstrate suitable demand forecasting methodologies. We apply linear and nonlinear forecasting methods to a three-year time ... -
VisExpA: Visibility expansion algorithm in the topology of complex networks
(2020)In this study, we provide the VisExpA (Visibility Expansion Algorithm), a computational code that implements a recently published method, which allows generating a visibility graph from a complex network instead of a ... -
Visibility in the topology of complex networks
(2018)Taking its inspiration from the visibility algorithm, which was proposed by Lacasa et al. (2008) to convert a time-series into a complex network, this paper develops and proposes a novel expansion of this algorithm that ...