782 research outputs found
High roughness time series forecasting based on energy associated of series
In this study, an algorithm to adjust parameters of high roughness time series based on energy associated of series using a feed-forward NN-based model is presented. The criterion for adjustment consists of building time series values from forecasted time series area and taking into account the roughness of series. These values are approximated by the NN to make a primitive calculated as an area by the predictor filter used as a new entrance. A comparison between this work and another that involves a similar approach to test time series prediction, indicates an improvement for certain sort of series. The NN filter output is intended to approximate the current value available from the series which has the same Hurst Parameter as the real time series. The proposed approach is tested over five time series obtained from samples of Mackey-Glass delay differential equations (MG). Therefore, these results show a model performance for time series forecasting and encourage to be applied for meteorological variables measurements such as soil moisture series, daily rainfall and monthly cumulative rainfall time series forecasting.Fil: Rodriguez Rivero, Cristian Maximiliano. Universidad Nacional de CĂłrdoba. Facultad de Ciencias Exactas, FĂsicas y Naturales; ArgentinaFil: Pucheta, Julián Antonio. Universidad Nacional de CĂłrdoba. Facultad de Ciencias Exactas, FĂsicas y Naturales; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de IngenierĂa. Instituto de Automática; ArgentinaFil: Baumgartner, Josef Sylvester. Universidad Nacional de CĂłrdoba. Facultad de Cs.exactas FĂsicas y Naturales. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; ArgentinaFil: Patiño, HĂ©ctor Daniel. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de IngenierĂa. Instituto de Automática; ArgentinaFil: Sauchelli, Victor Hugo. Universidad Nacional de CĂłrdoba. Facultad de Ciencias Exactas, FĂsicas y Naturales; Argentin
Forecasting inflation with thick models and neural networks
This paper applies linear and neural network-based “thick” models for forecasting inflation based on Phillips–curve formulations in the USA, Japan and the euro area. Thick models represent “trimmed mean” forecasts from several neural network models. They outperform the best performing linear models for “real-time” and “bootstrap” forecasts for service indices for the euro area, and do well, sometimes better, for the more general consumer and producer price indices across a variety of countries. JEL Classification: C12, E31bootstrap, Neural Networks, Phillips Curves, real-time forecasting, Thick Models
Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations
The increased digitalisation and monitoring of the energy system opens up
numerous opportunities to decarbonise the energy system. Applications on low
voltage, local networks, such as community energy markets and smart storage
will facilitate decarbonisation, but they will require advanced control and
management. Reliable forecasting will be a necessary component of many of these
systems to anticipate key features and uncertainties. Despite this urgent need,
there has not yet been an extensive investigation into the current
state-of-the-art of low voltage level forecasts, other than at the smart meter
level. This paper aims to provide a comprehensive overview of the landscape,
current approaches, core applications, challenges and recommendations. Another
aim of this paper is to facilitate the continued improvement and advancement in
this area. To this end, the paper also surveys some of the most relevant and
promising trends. It establishes an open, community-driven list of the known
low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape
Multi-agent system for flood forecasting in Tropical River Basin
It is well known, the problems related to the generation of floods, their control, and management,
have been treated with traditional hydrologic modeling tools focused on the study and
the analysis of the precipitation-runoff relationship, a physical process which is driven by the
hydrological cycle and the climate regime and that is directly proportional to the generation
of floodwaters. Within the hydrological discipline, they classify these traditional modeling
tools according to three principal groups, being the first group defined as trial-and-error models
(e.g., "black-models"), the second group are the conceptual models, which are categorized
in three main sub-groups as "lumped", "semi-lumped" and "semi-distributed", according to
the special distribution, and finally, models that are based on physical processes, known as
"white-box models" are the so-called "distributed-models". On the other hand, in engineering
applications, there are two types of models used in streamflow forecasting, and which are
classified concerning the type of measurements and variables required as "physically based
models", as well as "data-driven models".
The Physically oriented prototypes present an in-depth account of the dynamics related
to the physical aspects that occur internally among the different systems of a given hydrographic
basin. However, aside from being laborious to implement, they rely thoroughly
on mathematical algorithms, and an understanding of these interactions requires the abstraction
of mathematical concepts and the conceptualization of the physical processes that
are intertwined among these systems. Besides, models determined by data necessitates an
a-priori understanding of the physical laws controlling the process within the system, and
they are bound to mathematical formulations, which require a lot of numeric information
for field adjustments. Therefore, these models are remarkably different from each other
because of their needs for data, and their interpretation of physical phenomena. Although
there is considerable progress in hydrologic modeling for flood forecasting, several significant
setbacks remain unresolved, given the stochastic nature of the hydrological phenomena, is
the challenge to implement user-friendly, re-usable, robust, and reliable forecasting systems,
the amount of uncertainty they must deal with when trying to solve the flood forecasting
problem. However, in the past decades, with the growing environment and development of
the artificial intelligence (AI) field, some researchers have seldomly attempted to deal with
the stochastic nature of hydrologic events with the application of some of these techniques.
Given the setbacks to hydrologic flood forecasting previously described this thesis research
aims to integrate the physics-based hydrologic, hydraulic, and data-driven models under the
paradigm of Multi-agent Systems for flood forecasting by designing and developing a multi-agent system (MAS) framework for flood forecasting events within the scope of tropical
watersheds.
With the emergence of the agent technologies, the "agent-based modeling" and "multiagent
systems" simulation methods have provided applications for some areas of hydro base
management like flood protection, planning, control, management, mitigation, and forecasting
to combat the shocks produced by floods on society; however, all these focused on
evacuation drills, and the latter not aimed at the tropical river basin, whose hydrological
regime is extremely unique.
In this catchment modeling environment approach, it was applied the multi-agent systems
approach as a surrogate of the conventional hydrologic model to build a system that operates
at the catchment level displayed with hydrometric stations, that use the data from hydrometric
sensors networks (e.g., rainfall, river stage, river flow) captured, stored and administered
by an organization of interacting agents whose main aim is to perform flow forecasting and
awareness, and in so doing enhance the policy-making process at the watershed level.
Section one of this document surveys the status of the current research in hydrologic
modeling for the flood forecasting task. It is a journey through the background of related
concerns to the hydrological process, flood ontologies, management, and forecasting. The
section covers, to a certain extent, the techniques, methods, and theoretical aspects and
methods of hydrological modeling and their types, from the conventional models to the
present-day artificial intelligence prototypes, making special emphasis on the multi-agent
systems, as most recent modeling methodology in the hydrological sciences. However, it is
also underlined here that the section does not contribute to an all-inclusive revision, rather
its purpose is to serve as a framework for this sort of work and a path to underline the
significant aspects of the works.
In section two of the document, it is detailed the conceptual framework for the suggested
Multiagent system in support of flood forecasting. To accomplish this task, several works
need to be carried out such as the sketching and implementation of the system’s framework
with the (Belief-Desire-Intention model) architecture for flood forecasting events within the
concept of the tropical river basin. Contributions of this proposed architecture are the
replacement of the conventional hydrologic modeling with the use of multi-agent systems,
which makes it quick for hydrometric time-series data administration and modeling of the
precipitation-runoff process which conveys to flood in a river course. Another advantage is
the user-friendly environment provided by the proposed multi-agent system platform graphical
interface, the real-time generation of graphs, charts, and monitors with the information
on the immediate event taking place in the catchment, which makes it easy for the viewer
with some or no background in data analysis and their interpretation to get a visual idea of
the information at hand regarding the flood awareness.
The required agents developed in this multi-agent system modeling framework for flood
forecasting have been trained, tested, and validated under a series of experimental tasks,
using the hydrometric series information of rainfall, river stage, and streamflow data collected
by the hydrometric sensor agents from the hydrometric sensors.Como se sabe, los problemas relacionados con la generaciĂłn de inundaciones, su control y
manejo, han sido tratados con herramientas tradicionales de modelado hidrolĂłgico enfocados
al estudio y análisis de la relaciĂłn precipitaciĂłn-escorrentĂa, proceso fĂsico que es impulsado
por el ciclo hidrológico y el régimen climático y este esta directamente proporcional a la
generaciĂłn de crecidas. Dentro de la disciplina hidrolĂłgica, clasifican estas herramientas
de modelado tradicionales en tres grupos principales, siendo el primer grupo el de modelos
empĂricos (modelos de caja negra), modelos conceptuales (o agrupados, semi-agrupados o
semi-distribuidos) dependiendo de la distribuciĂłn espacial y, por Ăşltimo, los basados en la
fĂsica, modelos de proceso (o "modelos de caja blanca", y/o distribuidos). En este sentido,
clasifican las aplicaciones de predicciĂłn de caudal fluvial en la ingenierĂa de recursos hĂdricos
en dos tipos con respecto a los valores y parámetros que requieren en: modelos de procesos
basados en la fĂsica y la categorĂa de modelos impulsados por datos.
Los modelos basados en la fĂsica proporcionan una descripciĂłn detallada de la dinámica
relacionada con los aspectos fĂsicos que ocurren internamente entre los diferentes sistemas de
una cuenca hidrográfica determinada. Sin embargo, aparte de ser complejos de implementar,
se basan completamente en algoritmos matemáticos, y la comprensión de estas interacciones
requiere la abstracción de conceptos matemáticos y la conceptualización de los procesos
fĂsicos que se entrelazan entre estos sistemas. Además, los modelos impulsados por datos no
requieren conocimiento de los procesos fĂsicos que gobiernan, sino que se basan Ăşnicamente
en ecuaciones empĂricas que necesitan una gran cantidad de datos y requieren calibraciĂłn
de los datos en el sitio. Los dos modelos difieren significativamente debido a sus requisitos
de datos y de cĂłmo expresan los fenĂłmenos fĂsicos. La elaboraciĂłn de modelos hidrolĂłgicos
para el pronĂłstico de inundaciones ha dado grandes pasos, pero siguen sin resolverse algunos
contratiempos importantes, dada la naturaleza estocástica de los fenómenos hidrológicos, es
el desafĂo de implementar sistemas de pronĂłstico fáciles de usar, reutilizables, robustos y
confiables, la cantidad de incertidumbre que deben afrontar al intentar resolver el problema
de la predicción de inundaciones. Sin embargo, en las últimas décadas, con el entorno
creciente y el desarrollo del campo de la inteligencia artificial (IA), algunos investigadores
rara vez han intentado abordar la naturaleza estocástica de los eventos hidrológicos con la
aplicación de algunas de estas técnicas.
Dados los contratiempos en el pronĂłstico de inundaciones hidrolĂłgicas descritos anteriormente,
esta investigaciĂłn de tesis tiene como objetivo integrar los modelos hidrolĂłgicos,
basados en la fĂsica, hidráulicos e impulsados por datos bajo el paradigma de Sistemas de mĂşltiples agentes para el pronĂłstico de inundaciones por medio del bosquejo y desarrollo
del marco de trabajo del sistema multi-agente (MAS) para los eventos de predicciĂłn de
inundaciones en el contexto de cuenca hidrográfica tropical.
Con la apariciĂłn de las tecnologĂas de agentes, se han emprendido algunos enfoques
de simulaciĂłn recientes en la investigaciĂłn hidrolĂłgica con modelos basados en agentes y
sistema multi-agente, principalmente en alerta por inundaciones, seguridad y planificaciĂłn
de inundaciones, control y gestiĂłn de inundaciones y pronĂłstico de inundaciones, todos estos
enfocado a simulacros de evacuaciĂłn, y este Ăşltimo no dirigido a la cuenca tropical, cuyo
régimen hidrológico es extremadamente único.
En este enfoque de entorno de modelado de cuencas, se aplican los enfoques de sistemas
multi-agente como un sustituto del modelado hidrolĂłgico convencional para construir un
sistema que opera a nivel de cuenca con estaciones hidrométricas desplegadas, que utilizan
los datos de redes de sensores hidromĂ©tricos (por ejemplo, lluvia , nivel del rĂo, caudal del
rĂo) capturado, almacenado y administrado por una organizaciĂłn de agentes interactuantes
cuyo objetivo principal es realizar pronĂłsticos de caudal y concientizaciĂłn para mejorar las
capacidades de soporte en la formulaciĂłn de polĂticas a nivel de cuenca hidrográfica.
La primera secciĂłn de este documento analiza el estado del arte sobre la investigaciĂłn actual
en modelos hidrológicos para la tarea de pronóstico de inundaciones. Es un viaje a través
de los antecedentes preocupantes relacionadas con el proceso hidrolĂłgico, las ontologĂas de
inundaciones, la gestión y la predicción. El apartado abarca, en cierta medida, las técnicas,
mĂ©todos y aspectos teĂłricos y mĂ©todos del modelado hidrolĂłgico y sus tipologĂas, desde
los modelos convencionales hasta los prototipos de inteligencia artificial actuales, haciendo
hincapié en los sistemas multi-agente, como un enfoque de simulación reciente en la investigación
hidrolĂłgica. Sin embargo, se destaca que esta secciĂłn no contribuye a una revisiĂłn
integral, sino que su propĂłsito es servir de marco para este tipo de trabajos y una guĂa para
subrayar los aspectos significativos de los trabajos.
En la secciĂłn dos del documento, se detalla el marco de trabajo propuesto para el sistema
multi-agente para el pronĂłstico de inundaciones. Los trabajos realizados comprendieron el
diseño y desarrollo del marco de trabajo del sistema multi-agente con la arquitectura (modelo
Creencia-Deseo-IntenciĂłn) para la predicciĂłn de eventos de crecidas dentro del concepto
de cuenca hidrográfica tropical. Las contribuciones de esta arquitectura propuesta son el
reemplazo del modelado hidrolĂłgico convencional con el uso de sistemas multi-agente, lo
que agiliza la administración de las series de tiempo de datos hidrométricos y el modelado
del proceso de precipitaciĂłn-escorrentĂa que conduce a la inundaciĂłn en el curso de un rĂo.
Otra ventaja es el entorno amigable proporcionado por la interfaz gráfica de la plataforma del
sistema multi-agente propuesto, la generación en tiempo real de gráficos, cuadros y monitores
con la informaciĂłn sobre el evento inmediato que tiene lugar en la cuenca, lo que lo hace
fácil para el espectador con algo o sin experiencia en análisis de datos y su interpretación
para tener una idea visual de la informaciĂłn disponible con respecto a la cogniciĂłn de las
inundaciones.
Los agentes necesarios desarrollados en este marco de modelado de sistemas multi-agente
para el pronóstico de inundaciones han sido entrenados, probados y validados en una serie de tareas experimentales, utilizando la información de la serie hidrométrica de datos de lluvia,
nivel del rĂo y flujo del curso de agua recolectados por los agentes sensores hidromĂ©tricos de
los sensores hidromĂ©tricos de campo.Programa de Doctorado en Ciencia y TecnologĂa Informática por la Universidad Carlos III de MadridPresidente: MarĂa Araceli Sanchis de Miguel.- Secretario: Juan GĂłmez Romero.- Vocal: Juan Carlos Corrale
Learning-Based Modeling of Weather and Climate Events Related To El Niño Phenomenon via Differentiable Programming and Empirical Decompositions
This dissertation is the accumulation of the application of adaptive, empirical learning-based methods in the study and characterization of the El Niño Southern Oscillation. In specific, it focuses on ENSO’s effects on rainfall and drought conditions in two major regions shown to be linked through the strength of the dependence of their climate on ENSO: 1) the southern Pacific Coast of the United States and 2) the Nile River Basin. In these cases, drought and rainfall are tied to deep economic and social factors within the region. The principal aim of this dissertation is to establish, with scientific rigor, an epistemological and foundational justification of adaptive learning models and their utility in the both the modeling and understanding of a wide-reaching climate phenomenon such as ENSO. This dissertation explores a scientific justification for their proven accuracy in prediction and utility as an aide in deriving a deeper understanding of climate phenomenon. In the application of drought forecasting for Southern California, adaptive learning methods were able to forecast the drought severity of the 2015-2016 winter with greater accuracy than established models. Expanding this analysis yields novel ways to analyze and understand the underlying processes driving California drought. The pursuit of adaptive learning as a guiding tool would also lead to the discovery of a significant extractable components of ENSO strength variation, which are used with in the analysis of Nile River Basin precipitation and flow of the Nile River, and in the prediction of Nile River yield to p=0.038. In this dissertation, the duality of modeling and understanding is explored, as well as a discussion on why adaptive learning methods are uniquely suited to the study of climate phenomenon like ENSO in the way that traditional methods lack. The main methods explored are 1) differentiable Programming, as a means of construction of novel self-learning models through which the meaningfulness of parameters arises from emergent phenomenon and 2) empirical decompositions, which are driven by an adaptive rather than rigid component extraction principle, are explored further as both a predictive tool and as a tool for gaining insight and the construction of models
Using Self-Organizing Maps to Investigate Extreme Climate Events: An Application to Wintertime Precipitation in the Balkans
The International Society for Burns Injuries (ISBI) has published guidelines for the management of multiple or mass burns casualties, and recommends that 'each country has or should have a disaster planning system that addresses its own particular needs.' The need for a national burns disaster plan integrated with national and provincial disaster planning was discussed at the South African Burns Society Congress in 2009, but there was no real involvement in the disaster planning prior to the 2010 World Cup; the country would have been poorly prepared had there been a burns disaster during the event. This article identifies some of the lessons learnt and strategies derived from major burns disasters and burns disaster planning from other regions. Members of the South African Burns Society are undertaking an audit of burns care in South Africa to investigate the feasibility of a national burns disaster plan. This audit (which is still under way) also aims to identify weaknesses of burns care in South Africa and implement improvements where necessary
Some Essays on models in the Bond and Energy Markets
The term structure of interest rates plays a fundamental role as an indicator of economy and market trends, as well as a supporting tool for macroeconomic strategies, investment choices or hedging practices. Therefore, the availability of proper techniques to model and predict its dynamics is of crucial importance for players in the financial markets. Along this path, the dissertation initially examined the reliability of parametric and neural network models to fit and predict the term structure of interest rates in emerging markets, focusing on the Brazilian, Russian, Indian, Chines and South African (BRICS) bond markets. The focus on the BRICS is straightforward: the dynamics of their term structures make tricky the application of consolidated yield curve models. In this respect, BRICS yield curve act as stress testers. The study then examined how to apply the above cited models to energy derivatives, focusing the attention on the Natural Gas and Electricity futures, motivated by the existence of similarity. The research was carried out using ad hoc routines, such as the R package "DeRezende.Ferreira", developed by the candidate and now freely downloadable at the Comprehensive R Archive Network (CRAN) repository*, as well as by means of code written in MatLab 2021a - 2022a and Python (3.10.10) using the open-source Keras (2.4.3) library with TensorFlow (2.4.0) as backend. The dissertation consists of four chapters based on published and/or under submission materials. Chapter 1 is an excerpt of the paper • Castello, O.; Resta, M. Modeling the Yield Curve of BRICS Countries: Parametric vs. Machine Learning Techniques. Risks 2022
The work firstly offers a comprehensive analysis of the BRICS bond market and then investigates and compares the abilities of the parametric Five–Factor De Rezende–Ferreira model and Feed–Forward Neural Networks to fit the yield curves. Chapter 2 is again focused on the BRICS market but investigates a methodology to identify optimal time–varying parameters for parametric yield curve models. The work then investigates the ability of this method both for in–sample fitting and out–of–sample prediction. Various forecasting methods are examined: the Univariate Autoregressive process AR(1), the TBATS and the Autoregressive Integrated Moving Average (ARIMA) combined to Nonlinear Autoregressive Neural Networks (NAR–NN). Chapter 3 studies the term structure dynamics in the Natural Gas futures market. This chapter represents an extension of the paper • Castello, O., Resta, M. (2022). Modeling and Forecasting Natural Gas Futures Prices Dynamics: An Integrated Approach. In: Corazza, M., Perna, C., Pizzi, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2022.
After showing that the natural gas and bond markets share similar stylized facts, we exploit these findings to examine whether techniques conventionally employed on the bonds market can be effectively used also for accurate in–sample fitting and out–of–sample forecast. We worked at first in–sample and we compared the performance of three models: the Four–Factor Dynamic Nelson–Siegel–Svensson (4F-DNSS), the Five–Factor Dynamic De Rezende–Ferreira (5F–DRF) and the B–Spline. Then, we turned the attention on forecasting, and explored the effectiveness of a hybrid methodology relying on the joint use of 4F–DNSS, 5F–DRF and B–Splines with Nonlinear Autoregressive Neural Networks (NAR–NNs). Empirical study was carried on using the Dutch Title Transfer Facility (TTF) daily futures prices in the period from January 2011 to June 2022 which included also recent market turmoil to validate the overall effectiveness of the framework.
Chapter 4 analyzes the predictability of the electricity futures prices term structure with Artificial Neural Networks. Prices time series and futures curves are characterized by high volatility which is a direct consequence of an inelastic demand and of the non–storable nature of the underlying commodity. We analyzed the forecasting power of several neural network models, including Nonlinear Autoregressive (NAR–NNs), NAR with Exogenous Inputs (NARX–NNs), Long Short–Term Memory (LSTM–NNs) and Encoder–Decoder Long Short–Term Memory Neural Networks (ED–LSTM–NNs). We carried out an extensive study of the models predictive capabilities using both the univariate and multivariate setting. Additionally, we explored whether incorporating various exogenous components such as Carbon Emission Certificates (CO2) spot prices,
as well as Natural Gas and Coal futures prices can lead to improvements of the models performances. The data of the European Energy Exchange (EEX) power market were adopted to test the models. Chapter 4 concludes.
____________________________ * https://cran.r-project.org/web/packages/DeRezende.Ferreira/index.htm
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