A study of comparative forecasting.

During the past two decades, there has been an increasing number of comparative forecasting studies. The objective of these studies is to compare different forecasting methodologies with the hope of finding the best methodology. These studies have led to conflicting reports and controversies.This dissertation examines almost all published comparative studies and delineates a list of fallacies occurring in comparative forecasting studies. These fallacies most commonly give rise to the existing controversies. Since the controversies in forecasting stem from comparisons of the various approaches, a brief synopsis of the most currently employed univariate and multivariate methodologies are presented

Widely Linear State Space Filtering of Improper Complex Signals

Complex signals are the backbone of many modern applications, such as power systems, communication systems, biomedical sciences and military technologies. However, standard complex valued signal processing approaches are suited to only a subset of complex signals known as proper, and are inadequate of the generality of complex signals, as they do not fully exploit the available information. This is mainly due to the inherent blindness of the algorithms to the complete second order statistics of the signals, or due to under-modelling of the underlying system. The aim of this thesis is to provide enhanced complex valued, state space based, signal processing solutions for the generality of complex signals and systems. This is achieved based on the recent advances in the so called augmented complex statistics and widely linear modelling, which have brought to light the limitations of conventional statistical complex signal processing approaches. Exploiting these developments, we propose a class of widely linear adaptive state space estimation techniques, which provide a unified framework and enhanced performance for the generality of complex signals, compared with conventional approaches. These include the linear and nonlinear Kalman and particle filters, whereby it is shown that catering for the complete second order information and system models leads to significant performance gains. The proposed techniques are also extended to the case of cooperative distributed estimation, where nodes in a network collaborate locally to estimate signals, under a framework that caters for general complex signals, as well as the cross-correlations between observation noises, unlike earlier solutions. The analysis of the algorithms are supported by numerous case studies, including frequency estimation in three phase power systems, DIFAR sonobuoy underwater target tracking, and real-world wind modeling and prediction.Open Acces

Connected and Autonomous Vehicles Applications Development and Evaluation for Transportation Cyber-Physical Systems

Cyber-Physical Systems (CPS) seamlessly integrate computation, networking and physical devices. A Connected and Autonomous Vehicle (CAV) system in which each vehicle can wirelessly communicate and share data with other vehicles or infrastructures (e.g., traffic signal, roadside unit), requires a Transportation Cyber-Physical System (TCPS) for improving safety and mobility, and reducing greenhouse gas emissions. Unfortunately, a typical TCPS with a centralized computing service cannot support real-time CAV applications due to the often unpredictable network latency, high data loss rate and expensive communication bandwidth, especially in a mobile network, such as a CAV environment. Edge computing, a new concept for the CPS, distributes the resources for communication, computation, control, and storage at different edges of the systems. TCPS with edge computing strategy forms an edge-centric TCPS. This edge-centric TCPS system can reduce data loss and data delivery delay, and fulfill the high bandwidth requirements. Within the edge-centric TCPS, Vehicle-to-X (V2X) communication, along with the in-vehicle sensors, provides a 360-degree view for CAVs that enables autonomous vehicles’ operation beyond the sensor range. The addition of wireless connectivity would improve the operational efficiency of CAVs by providing real-time roadway information, such as traffic signal phasing and timing information, downstream traffic incident alerts, and predicting future traffic queue information. In addition, temporal variation of roadway traffic can be captured by sharing Basic Safety Messages (BSMs) from each vehicle through the communication between vehicles as well as with roadside infrastructures (e.g., traffic signal, roadside unit) and traffic management centers. In the early days of CAVs, data will be collected only from a limited number of CAVs due to a low CAV penetration rate and not from other non-connected vehicles. This will result in noise in the traffic data because of low penetration rate of CAVs. This lack of data combined with the data loss rate in the wireless CAV environment makes it challenging to predict traffic behavior, which is dynamic over time. To address this challenge, it is important to develop and evaluate a machine learning technique to capture stochastic variation in traffic patterns over time. This dissertation focuses on the development and evaluation of various connected and autonomous vehicles applications in an edge-centric TCPS. It includes adaptive queue prediction, traffic data prediction, dynamic routing and Cooperative Adaptive Cruise Control (CACC) applications. An adaptive queue prediction algorithm is described in Chapter 2 for predicting real-time traffic queue status in an edge-centric TCPS. Chapter 3 presents noise reduction models to reduce the noise from the traffic data generated from the BSMs at different penetration of CAVs and evaluate the performance of the Long Short-Term Memory (LSTM) prediction model for predicting traffic data using the resulting filtered data set. The development and evaluation of a dynamic routing application in a CV environment is detailed in Chapter 4 to reduce incident recovery time and increase safety on a freeway. The development of an evaluation framework is detailed in Chapter 5 to evaluate car-following models for CACC controller design in terms of vehicle dynamics and string stability to ensure user acceptance is detailed in Chapter 5. Innovative methods presented in this dissertation were proven to be providing positive improvements in transportation mobility. These research will lead to the real-world deployment of these applications in an edge-centric TCPS as the dissertation focuses on the edge-centric TCPS deployment strategy. In addition, as multiple CAV applications as presented in this dissertation can be supported simultaneously by the same TCPS, public investments will only include infrastructure investments, such as investments in roadside infrastructure and back-end computing infrastructure. These connected and autonomous vehicle applications can potentially provide significant economic benefits compared to its cost

State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand

Multiresolution image modelling and estimation

Multiresolution representations make explicit the notion of scale in images, and facilitate the combination of information from different scales. To date, however, image modelling and estimation schemes have not exploited such representations and tend rather to be derived from two- dimensional extensions of traditional one-dimensional signal processing techniques. In the causal case, autoregressive (AR) and ARMA models lead to minimum mean square error (MMSE) estimators which are two-dimensional variants of the well-established Kalman filter. Noncausal approaches tend to be transform-based and the MMSE estimator is the two- dimensional Wiener filter. However, images contain profound nonstationarities such as edges, which are beyond the descriptive capacity of such signal models, and defects such as blurring (and streaking in the causal case) are apparent in the results obtained by the associated estimators. This thesis introduces a new multiresolution image model, defined on the quadtree data structure. The model is a one-dimensional, first-order gaussian martingale process causal in the scale dimension. The generated image, however, is noncausal and exhibits correlations at all scales unlike those generated by traditional models. The model is capable of nonstationary behaviour in all three dimensions (two position and one scale) and behaves isomorphically but independently at each scale, in keeping with the notion of scale invariance in natural images. The optimal (MMSE) estimator is derived for the case of corruption by additive white gaussian noise (AWGN). The estimator is a one-dimensional, first-order linear recursive filter with a computational burden far lower than that of traditional estimators. However, the simple quadtree data structure leads to aliasing and 'block' artifacts in the estimated images. This could be overcome by spatial filtering, but a faster method is introduced which requires no additional multiplications but involves the insertion of some extra nodes into the quadtree. Nonstationarity is introduced by a fast, scale-invariant activity detector defined on the quadtree. Activity at all scales is combined in order to achieve noise rejection. The estimator is modified at each scale and position by the detector output such that less smoothing is applied near edges and more in smooth regions. Results demonstrate performance superior to that of existing methods, and at drastically lower computational cost. The estimation scheme is further extended to include anisotropic processing, which has produced good results in image restoration. An orientation estimator controls anisotropic filtering, the output of which is made available to the image estimator

Path planning, flow estimation, and dynamic control for underwater vehicles

Underwater vehicles such as robotic fish and long-endurance ocean-sampling platforms operate in challenging fluid environments. This dissertation incorporates models of the fluid environment in the vehicles' guidance, navigation, and control strategies while addressing uncertainties associated with estimates of the environment's state. Coherent flow structures may be on the same spatial scale as the vehicle or substantially larger than the vehicle. This dissertation argues that estimation and control tasks across widely varying spatial scales, from vehicle-scale to long-range, may be addressed using common tools of empirical observability analysis, nonlinear/non-Gaussian estimation, and output-feedback control. As an application in vehicle-scale flow estimation and control, this dissertation details the design, fabrication, and testing of a robotic fish with an artificial lateral-line inspired by the lateral-line flow-sensing organ present in fish. The robotic fish is capable of estimating the flow speed and relative angle of the oncoming flow. Using symmetric and asymmetric sensor configurations, the robot achieves the primitive fish behavior called rheotaxis, which describes a fish's tendency to orient upstream. For long-range flow estimation and control, path planning may be accomplished using observability-based path planning, which evaluates a finite set of candidate control inputs using a measure related to flow-field observability and selects an optimizer over the set. To incorporate prior information, this dissertation derives an augmented observability Gramian using an optimal estimation strategy known as Incremental 4D-Var. Examination of the minimum eigenvalue of an empirical version of this Gramian yields a novel measure for path planning, called the empirical augmented unobservability index. Numerical experiments show that this measure correctly selects the most informative paths given the prior information. As an application in long-range flow estimation and control, this dissertation considers estimation of an idealized pair of ocean eddies by an adaptive Lagrangian sensor (i.e., a platform that uses its position data as measurements of the fluid transport, after accounting for its own control action). The adaptive sampling is accomplished using the empirical augmented unobservability index, which is extended to non-Gaussian posterior densities using an approximate expected-cost calculation. Output feedback recursively improves estimates of the vehicle position and flow-field states

Forecasting of uv-vis spectrometry time series for online water quality monitoring in operating urban sewer systems

El monitoreo de contaminantes en sistemas de saneamiento urbano es generalmente realizado por medio de campañas de muestreo, las muestras deben ser transportadas, almacenadas y analizadas en laboratorio. Sin embargo, los desarrollos en óptica y electrónica han permitido su fusión y aplicación en la espectrometría UV-Vis. Los sensores UV-Vis tienen como propósito determinar la dinámica de las cargas de materia orgánica (Demanda Química de Oxigeno DQO y Demanda Bioquímica de Oxigeno DBO5), nitratos, nitritos y Sólidos Suspendidos Totales (SST). Adicionalmente a los métodos aplicados para la calibración de los sensores y el análisis las series de tiempo de los espectros de absorbancias UV-Vis, es necesario desarrollar métodos de pronóstico con el fin de ser utilizada en control de monitoreo en línea en tiempo real. La información proveniente de los datos recolectados puede ser utilizada para la toma de decisiones y en aplicaciones de control de tiempo real. Realizar pronósticos es importante en procesos de toma de decisiones. Por lo tanto, el objetivo de este trabajo de investigación fue desarrollar uno o varios métodos de pronóstico que puedan ser aplicados a series de tiempo de espectrometría UV-Vis para el monitoreo en línea de la calidad de agua en sistemas urbanos de saneamiento en operación. Cinco series de tiempo de absorbancia UV-Vis obtenidas en línea en diferentes sitios fueron utilizadas, con un total de 5705 espectros de absorbancia UV-Vis: cuatro sitios experimentales en Colombia (Planta de Tratamiento de Aguas Residuales (PTAR) El-Salitre, PTAR San Fernando, Estación Elevadora de Gibraltar y un Humedal Construido/Tanque de Almacenamiento) y un sitio en Austria (Graz-West R05 Catchment outlet). El proceso propuesto completo consta de etapas a ser aplicadas a las series de tiempo de absorbancia UV-Vis y son: (i) entradas, series de tiempo de absorbancia UV-Vis,(ii) pre-procesamiento de las series de tiempo, análisis de outliers, completar los valores ausentes y reducción de la dimensionalidad,y (iii) procedimientos de pronóstico y evaluación de los resultados. La metodología propuesta fue aplicada a la series de tiempo con diferentes características (absorbancia), esta consiste del enventaneo Winsorising como paso para la remoción de outliers y la aplicación de la transformada discreta de Fourier (DFT) para reemplazar valores ausentes. Los nuevos valores reemplazando o los outliers o los valores ausentes presentan la misma o al menos la misma forma de la serie de tiempo original, permitiendo una visión macro en la coherencia de la serie de tiempo. La reducción de la dimensionalidad en las series de tiempo de absorbancia multivariadas permite obtener menor número de variables a ser procesadas: el análisis por componentes principales (PCA) como transformación lineal captura más del 97% de la variabilidad en cada serie de tiempo (en un rango de una a seis, dependiendo del comportamiento de la series de tiempo absorbancia) y el proceso de Clustering (k-means) combinado con cadenas de Markov. Los procedimientos de pronóstico basados en señales periódicas como la DFT, Chebyshev, Legendre y Regresión Polinomial fueron aplicados y estos pueden capturar el comportamiento dinámico de las series de tiempo. Algunas técnicas de aprendizaje de máquina fueron probadas y fue posible capturar el comportamiento de las series de tiempo en la etapa de calibración, los valores de pronóstico pueden seguir el comportamiento general comparado con los valores observados (excepto ANFIS, GA y Filtro de Kalman). Por lo tanto, ANN y SVM tiene buen rendimiento de pronóstico para la primer parte del horizonte de pronóstico (2 horas). La evaluación de cada metodología de pronóstico fue realizada utilizando cuatro indicadores estadísticos tales como porcentaje absoluto de error (APE), incertidumbre extendida (EU), conjunto de valores dentro del intervalo de confianza (CI) y suma de valores de incertidumbre extendida más el conjunto de valores dentro del intervalo de confianza. El rendimiento de los indicadores provee información acerca de los resultados de pronóstico multivariado con el fin de estimar y evaluar los tiempos de pronóstico para cierta metodología de pronóstico y determinar cuál metodología de pronóstico es mejor adaptada a diferentes rangos de longitudes de onda (espectros de absorbancia) para cada serie de tiempo de absorbancia UV-Vis en cada sitio de estudio. Los resultados en la comparación de las diferentes metodologías de pronóstico, resaltan que no es posible obtener la mejor metodología de pronóstico, porque todas las metodologías de pronóstico propuestas podrían generar un amplio número de valores que permitirán complementar cada una con las otras para diferentes pasos de tiempo de pronóstico y en diferentes rangos del espectro (UV y/o Vis). Por lo tanto, es propuesto un sistema híbrido que es basado en siete metodologías de pronóstico. Así, los valores de los espectros de absorbancia pronosticados fueron transformados a los correspondientes indicadores de calidad de agua (WQI) para utilización en la práctica. Los resultados de pronóstico multivariado presentan valores bajos de APE comparados con los resultados de pronóstico univariado utilizando directamente los valores WQI observados. Estos resultados, probablemente, son obtenidos porque el pronóstico multivariado incluye la correlación presente en todo el rango de los espectros de absorbancia (se captura de forma completa o al menos gran parte de la variabilidad de las series de tiempo),una longitud de onda interfiere con otra u otras longitudes de onda. Finalmente, los resultados obtenidos para el humedal construido/tanque de almacenamiento presentan que es posible obtener apreciables resultados de pronóstico en términos de tiempos de detección para eventos de lluvia. Adicionalmente, la inclusión de variables como escorrentía (nivel de agua para este caso) mejora substancialmente los resultados de pronóstico de la calidad del agua. El monitoreo de contaminantes en sistemas de saneamiento urbano es generalmente realizado por medio de campañas de muestreo, las muestras deben ser transportadas, almacenadas y analizadas en laboratorio. Sin embargo, los desarrollos en óptica y electrónica han permitido su fusión y aplicación en la espectrometría UV-Vis. Los sensores UV-Vis tienen como propósito determinar la dinámica de las cargas de materia orgánica (Demanda Química de Oxigeno DQO y Demanda Bioquímica de Oxigeno DBO5), nitratos, nitritos y Sólidos Suspendidos Totales (SST). Adicionalmente a los métodos aplicados para la calibración de los sensores y el análisis las series de tiempo de los espectros de absorbancias UV-Vis, es necesario desarrollar métodos de pronóstico con el fin de ser utilizada en control de monitoreo en línea en tiempo real. La información proveniente de los datos recolectados puede ser utilizada para la toma de decisiones y en aplicaciones de control de tiempo real. Realizar pronósticos es importante en procesos de toma de decisiones. Por lo tanto, el objetivo de este trabajo de investigación fue desarrollar uno o varios métodos de pronóstico que puedan ser aplicados a series de tiempo de espectrometría UV-Vis para el monitoreo en línea de la calidad de agua en sistemas urbanos de saneamiento en operación. Cinco series de tiempo de absorbancia UV-Vis obtenidas en línea en diferentes sitios fueron utilizadas, con un total de 5705 espectros de absorbancia UV-Vis: cuatro sitios experimentales en Colombia (Planta de Tratamiento de Aguas Residuales (PTAR) El-Salitre, PTAR San Fernando, Estación Elevadora de Gibraltar y un Humedal Construido/Tanque de Almacenamiento) y un sitio en Austria (Graz-West R05 Catchment outlet). El proceso propuesto completo consta de etapas a ser aplicadas a las series de tiempo de absorbancia UV-Vis y son: (i) entradas, series de tiempo de absorbancia UV-Vis,(ii) pre-procesamiento de las series de tiempo, análisis de outliers, completar los valores ausentes y reducción de la dimensionalidad,y (iii) procedimientos de pronóstico y evaluación de los resultados. La metodología propuesta fue aplicada a la series de tiempo con diferentes características (absorbancia), esta consiste del enventaneo Winsorising como paso para la remoción de outliers y la aplicación de la transformada discreta de Fourier (DFT) para reemplazar valores ausentes. Los nuevos valores reemplazando o los outliers o los valores ausentes presentan la misma o al menos la misma forma de la serie de tiempo original, permitiendo una visión macro en la coherencia de la serie de tiempo. La reducción de la dimensionalidad en las series de tiempo de absorbancia multivariadas permite obtener menor número de variables a ser procesadas: el análisis por componentes principales (PCA) como transformación lineal captura más del 97% de la variabilidad en cada serie de tiempo (en un rango de una a seis, dependiendo del comportamiento de la series de tiempo absorbancia) y el proceso de Clustering (k-means) combinado con cadenas de Markov. Los procedimientos de pronóstico basados en señales periódicas como la DFT, Chebyshev, Legendre y Regresión Polinomial fueron aplicados y estos pueden capturar el comportamiento dinámico de las series de tiempo. Algunas técnicas de aprendizaje de máquina fueron probadas y fue posible capturar el comportamiento de las series de tiempo en la etapa de calibración, los valores de pronóstico pueden seguir el comportamiento general comparado con los valores observados (excepto ANFIS, GA y Filtro de Kalman). Por lo tanto, ANN y SVM tiene buen rendimiento de pronóstico para la primer parte del horizonte de pronóstico (2 horas). La evaluación de cada metodología de pronóstico fue realizada utilizando cuatro indicadores estadísticos tales como porcentaje absoluto de error (APE), incertidumbre extendida (EU), conjunto de valores dentro del intervalo de confianza (CI) y suma de valores de incertidumbre extendida más el conjunto de valores dentro del intervalo de confianza. El rendimiento de los indicadores provee información acerca de los resultados de pronóstico multivariado con el fin de estimar y evaluar los tiempos de pronóstico para cierta metodología de pronóstico y determinar cuál metodología de pronóstico es mejor adaptada a diferentes rangos de longitudes de onda (espectros de absorbancia) para cada serie de tiempo de absorbancia UV-Vis en cada sitio de estudio. Los resultados en la comparación de las diferentes metodologías de pronóstico, resaltan que no es posible obtener la mejor metodología de pronóstico, porque todas las metodologías de pronóstico propuestas podrían generar un amplio número de valores que permitirán complementar cada una con las otras para diferentes pasos de tiempo de pronóstico y en diferentes rangos del espectro (UV y/o Vis). Por lo tanto, es propuesto un sistema híbrido que es basado en siete metodologías de pronóstico. Así, los valores de los espectros de absorbancia pronosticados fueron transformados a los correspondientes indicadores de calidad de agua (WQI) para utilización en la práctica. Los resultados de pronóstico multivariado presentan valores bajos de APE comparados con los resultados de pronóstico univariado utilizando directamente los valores WQI observados. Estos resultados, probablemente, son obtenidos porque el pronóstico multivariado incluye la correlación presente en todo el rango de los espectros de absorbancia (se captura de forma completa o al menos gran parte de la variabilidad de las series de tiempo),una longitud de onda interfiere con otra u otras longitudes de onda. Finalmente, los resultados obtenidos para el humedal construido/tanque de almacenamiento presentan que es posible obtener apreciables resultados de pronóstico en términos de tiempos de detección para eventos de lluvia. Adicionalmente, la inclusión de variables como escorrentía (nivel de agua para este caso) mejora substancialmente los resultados de pronóstico de la calidad del agua.The monitoring of pollutants in urban sewer systems is generally conducted by sampling campaigns, and the resulting samples must be transported, stored and analyzed in laboratory. However, the developments in optics and electronics have enabled the merge of them into the UV-Vis Spectrometry. UV-Vis probes have the purpose of determining the dynamics of loads of organic materials (i.e. Chemical Oxygen Demand (COD) and Biochemical Oxygen Demand (BOD5)), nitrates, nitrites and Total Suspended Solids (TSS). In addition to the methods used for the calibration of the probes and the analysis of the time series of UV-Vis absorbance spectra, it is necessary to develop forecasting methods in order to use the online control monitoring in real time. The information from the collected data can also be used for decision making purposes and for real-time control applications. Forecasting is important for decision-making processes. Therefore, the objective of this research work was to develop either a forecasting method or forecasting methods applied to UV-Vis spectrometry time series data for online water quality monitoring in operating urban sewer systems. Five UV-Vis Absorbance time series collected at different on-line measurement sites were used, for a total of 5705 UV-Vis absorbance spectra data: four sites in Colombia (El-Salitre Wastewater Treatment Plant-WWTP, San Fernando WWTP, Pumping Station (PS) sewage called Gibraltar and constructed-wetland/reservoir-tank (CWRT)) and one site in Austria (Graz-West R05 Catchment outlet). The complete process proposed to be applied to UV-Vis absorbance time series has several stages and these are: (i) inputs, the UV-Vis absorbance time series,(ii) the time series pre-processing, outliers analysis, complete missing values and time series dimensionality reduction,and (iii) forecasting procedures and evaluation of results. The methodology proposed was applied to the time series with different characteristics (absorbance), this consists of Winsorising as a step in outlier removal and the application of the Discrete Fourier Transform (DFT) to complete the missing values. The new values replaced either outliers or missing values present the same, or almost the same, shape as the original time series, granted the macro vision of the time series coherence. Dimensionality reduction of multivariate absorbance time series allows to have less variables to be processed: PCA linear transformation captures more than 97% of variability for each time series (PC ranging from one to six, depending on absorbance time series behavior), and Clustering process (k-means) combined with Markov Chains. Forecasting procedures based on periodic signals as DFT, Chebyshev, Legendre and Polynomial Regression were applied and they can capture the dynamic behaviour of the time series. Several Machine Learning technics were tested and it was possible to capture the behaviour of the time series at calibration stage, the forecasting obtained valúes can follow the general behaviour compared with observed valúes (with exception of ANFIS, GA and Kalman Filter). Therefore, ANN and SVM have good forecasting performances for first part of forecasting horizon (2 hours). The evaluation of each forecasting methodology was done using four statistic indicators as Absolute Percentage Error (APE), Extended Uncertainty (EU), Set of observed values within Confidence Interval (CI) and sum of EU and Set of observed values within CI. The performance indicators provided valuable information about multivariate forecasting results to estimate and evaluate the forecasting time for a given forecasting methodology and determine which forecasting methodology is best suited for different wavelength ranges (absorbance spectra) at each study site s UV-Vis absorbance time series. Results from different comparison of several forecasting methodologies, highlight that there is not possibility to have a best forecasting methodology among the proposed ones, because all of them could provide a wide forecasting values that would complemented each other for different forecasting time steps and spectra range (UV and/or Vis). Therefore, it is proposed a hybrid system that is based on seven forecasting methodologies. Thus, the forecasted absorbance spectra were transformed to Water Quality Indicators (WQI) for practical uses. The multivariate forecasting results show lower APE values compared to the univariate forecasting results (APE values) using the observed WQI. These results, probably, were obtained because multivariate forecasting includes the correlation presented at whole absorbance spectra range (captures complete or at least great part of time series variability),one wavelength interferes with another and/or other wavelengths. Finally, the results obtained for a constructed-wetland/reservoir-tank system show that it is possible to obtain valuable forecasting results in terms of time detection for some rainfall events. In addition, the inclusion of runoff variables (water level in this case) improves the water quality forecasting results.Doctor en IngenieríaDoctorad