Enric Monte's research while affiliated with Universitat Politècnica de Catalunya and other places
What is this page?
This page lists the scientific contributions of an author, who either does not have a ResearchGate profile, or has not yet added these contributions to their profile.
It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible. In doing so, we process publicly available (personal) data relating to the author as a member of the scientific community.
If you're a ResearchGate member, you can follow this page to keep up with this author's work.
If you are this author, and you don't want us to display this page anymore, please let us know.
It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible. In doing so, we process publicly available (personal) data relating to the author as a member of the scientific community.
If you're a ResearchGate member, you can follow this page to keep up with this author's work.
If you are this author, and you don't want us to display this page anymore, please let us know.
Publications (74)
We apply a soft computing method to generate country-specific economic sentiment indicators that provide estimates of year-on-year GDP growth rates for 19 European economies. First, genetic programming is used to evolve business and consumer economic expectations to derive sentiment indicators for each country. To assess the performance of the prop...
We apply the two-step machine-learning method proposed by Claveria et al. (2020) to generate country-specific sentiment indicators that provide estimates of year-on-year GDP growth rates. In the first step, by means of genetic programming, business and consumer expectations are evolved to derive sentiment indicators for 19 European economies. In th...
The main objective of this study is two-fold. First, we aim to detect the underlying existing periodicities in business and consumer survey expectations by means of spectral analysis. We use the Welch method to extract the harmonic components that correspond to the cyclic and seasonal patterns in all response options of monthly survey indicators. W...
In this study, we introduce a sentiment construction method based on the evolution of survey-based indicators. We make use of genetic algorithms to evolve qualitative expectations in order to generate country-specific empirical economic sentiment indicators in the three Baltic republics and the European Union. First, for each country we search for...
We apply the two-step machine-learning method proposed by Claveria et al. (2021) to generate country-specific sentiment indicators that provide estimates of year-on-year GDP growth rates. In the first step, by means of genetic programming, business and consumer expectations are evolved to derive sentiment indicators for 19 European economies. In th...
We present a machine-learning method for sentiment indicators construction that allows an automated variable selection procedure. By means of genetic programming, we generate country-specific business and consumer confidence indicators for thirteen European economies. The algorithm finds non-linear combinations of qualitative survey expectations th...
In this study we combine the results of two independent analyses to position Spanish regions according to both the characteristics of the time series of international tourist arrivals and the accuracy of predictions of arrivals at the regional level. We apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the...
In this study, we evaluate the effect of news on consumer unemployment expectations for sixteen socio-demographic groups. To this end, we construct an unemployment sentiment indicator and extract news about several economic variables. By means of genetic programming we estimate symbolic regressions that link unemployment rates in the Euro Area to q...
In this study we present a geometric approach to proxy economic uncertainty. We design a positional indicator of disagreement among survey-based agents’ expectations about the state of the economy. Previous dispersion-based uncertainty indicators derived from business and consumer surveys exclusively make use of the two extreme pieces of informatio...
In this study we use agents’ expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables...
The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, derivin...
In this study we present a geometric approach to proxy economic uncertainty. We design a positional indicator of disagreement among survey-based agents' expectations about the state of the economy. Previous dispersion-based uncertainty measures derived from business and consumer surveys exclusively make use of the two extreme pieces of information:...
In this study we present a geometric approach to proxy economic uncertainty. We design a positional indicator of disagreement among survey-based agents' expectations about the state of the economy. Previous dispersion-based uncertainty measures derived from business and consumer surveys exclusively make use of the two extreme pieces of information:...
This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recast accuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR...
In this study we present a geometric approach to proxy economic uncertainty. We design a positional indicator of disagreement among survey-based agents' expectations about the state of the economy. Previous dispersion-based uncertainty indicators derived from business and consumer surveys exclusively make of the information coming from the responde...
The main objective of this study is to present a two-step approach to generate estimates of economic growth based on agents’ expectations from tendency surveys. First, we design a genetic programming experiment to derive mathematical functional forms that approximate the target variable by combining survey data on expectations about different econo...
In this paper we propose a data-driven approach for the construction of survey-based indicators using large data sets. We make use of agents’ expectations about a wide range of economic variables contained in the World Economic Survey, which is a tendency survey conducted by the Ifo Institute for Economic Research. By means of genetic programming w...
This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and the Elman neural networks. The structure of the networks is based on a multiple-input multipl...
In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents? expectations. The research focuses on experts? expectations about the state of the economy from the World Economic Survey in...
Business and consumer surveys are the main source of agents' expectations. In this study we use survey expectations about a wide range of economic variables to forecast real activity. We propose an empirical approach to derive mathematical functional forms that link survey expectations to economic growth. Combining symbolic regression with genetic...
Machine learning (ML) methods are being increasingly used with forecasting purposes. This study assesses the predictive performance of several ML models in a multiple-input multiple-output (MIMO) setting that allows incorporating the cross-correlations between the inputs. We compare the forecast accuracy of a Gaussian process regression (GPR) model...
Agents’ perceptions on the state of the economy can be affected during economic crises. Tendency surveys are the main source of agents’ expectations. The main objective of this study is to assess the impact of the 2008 financial crisis on agents’ expectations. With this aim, we evaluate the capacity of survey-based expectations to anticipate econom...
Business and consumer surveys are the main source of agents’ expectations. In this study we use survey expectations about a wide range of economic variables to forecast GDP growth. We propose an empirical approach to derive mathematical functional forms that link survey-based expectations to present and future economic growth. Combining symbolic re...
In this study we use agents' expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables...
This study presents a multiple-input multiple-output (MIMO) approach for multi-step-ahead time series prediction with a Gaussian process regression (GPR) model. We assess the forecasting performance of the GPR model with respect to several neural network architectures. The MIMO setting allows modelling the cross-correlations between all regions sim...
In this study we use agents’ expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables...
This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recast accuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR...
This paper examines the role of clustering techniques to assist in the selection of the most indicated method to model survey-based expectations. First, relying on a Self-Organizing Map (SOM) analysis and using the financial crisis of 2008 as a benchmark, we distinguish between countries that show a progressive anticipation of the crisis, and count...
This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions...
Business and consumer surveys are the main source of agents' expectations. In this study we use survey expectations about a wide range of economic variables to forecast GDP growth. We propose a symbolic regression (SR) via genetic programming (GP) approach to derive mathematical functional forms that link survey-based expectations to present and fu...
The main objective of this study is to analyse whether the combination of regional predictions generated with machine learning (ML) models leads to improved forecast accuracy. With this aim, we construct one set of forecasts by estimating models on the aggregate series, another set by using the same models to forecast the individual series prior to...
Tendency surveys are the main source of agents’ expectations. This study has a twofold aim. First, it proposes a new method to quantify survey-based expectations by means of symbolic regression (SR) via genetic programming. Second, it combines the main SR-generated indicators to estimate the evolution of GDP, obtaining the best results for the Czec...
Purpose
– This study aims to apply a new forecasting approach to improve predictions in the hospitality industry. To do so, the authors developed a multivariate setting that allows the incorporation of the cross-correlations in the evolution of tourist arrivals from visitor markets to a specific destination in neural network models.
Design/methodo...
This study evaluates whether modelling the existing commont trends in tourism arrivals from all visitor markets to a specific destination can improve tourism predictions. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that take...
This paper aims to compare the performance of three different artificial neural network techniques for tourist demand forecasting: a multi-layer perceptron, a radial basis function and an Elman network. We find that multi-layer perceptron and radial basis function models outperform Elman networks. We repeated the experiment assuming different topol...
Provisioning tasteful services in the Cloud that guarantees high quality of service with reduced hosting cost is challenging to achieve. There are two typical auto-scaling approaches: predictive and reactive. A prediction based controller leaves the system enough time to react to workload changes while a feedback based controller scales the system...
By means of Self-Organizing Maps we cluster fourteen European countries according to the most suitable way to model their agents’ expectations. Using the financial crisis of 2008 as a benchmark, we distinguish between those countries that show a progressive anticipation of the crisis and those where sudden changes in expectations occur. By mapping...
This study attempts to assess the forecasting accuracy of Support Vector Regression (SVR) with regard to other Artificial Intelligence techniques based on statistical learning. We use two different neural networks and three SVR models that differ by the type of kernel used. We focus on international tourism demand to all seventeen regions of Spain....
While co-locating virtual machines improves utilization in resource shared environments, the resulting performance interference between VMs is difficult to model or predict. QoS sensitive applications can suffer from resource co-location with other less short-term resource sensitive or batch applications. The common practice of overprovisioning res...
This study compares the performance of different Artificial Neural Networks models for tourist demand forecasting in a multiple-output framework. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron network, a radial basis function network and an Elman neural network. We use official statistical data...
This paper aims to compare the performance of different Artificial Neural Networks techniques for tourist demand forecasting. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron, a radial basis function and an Elman network. We also evaluate the effect of the memory by repeating the experiment assumi...
This article presents a new approach to the problem of simultaneous tracking of several people in low-resolution sequences from multiple calibrated cameras. Redundancy among cameras is exploited to generate a discrete 3D colored representation of the scene, being the starting point of the processing chain. We review how the initiation and terminati...
Statistical machine translation (SMT) is based on alignment models which learn from bilingual corpora the word correspondences
between source and target language. These models are assumed to be capable of learning reorderings. However, the difference
in word order between two languages is one of the most important sources of errors in SMT. In this...
Tracking of unrestricted human movement has received great attention by the computer vision community basically fostered by the number of applications that benefit from it. Despite this research focus, there are few established mech-anisms for evaluating and comparing the performance of reported solutions. Existing metrics to quantify the perfor-ma...
In this work we discuss the development of two cross-lingual acoustic model sets for automatic speech recognition (ASR). The starting point is a set of multilingual Spanish-English-German hidden Markov models (HMMs). The target languages are Slovenian and French. During the discussion the problem of defining a multilingual phoneme set and the assoc...
In this work we focus on the construction of phonetic-acoustic decision trees for crosslingual use. Several modifications to the standard decision tree growing procedure are proposed aiming on integrating phonological and acoustical knowledge from source and target languages. This results in multilingual source models which already reflect characte...
The middle-term goal of this research project is to be able to recover several sound sources from a binaural life recording, by previously measuring the acoustic response of the room. As a previous step, this paper focuses on the reconstruction of n sources from mconvolutive mixtures when m < n (underdetermined case), assuming the mixing matrix is...
![Table 1 : Test results with WERs in [%].](profile/F-Diehl/publication/228567884/figure/tbl1/AS:669500391309364@1536632740729/Test-results-with-WERs-in_Q320.jpg)
In this work we present a novel adaptation design for semicontin-uous HMMs (SCHMM). The method, which is developed in the scope of a crosslingual model adaptation task, consists in adjust-ing the states' mixture weights associated to the prototype den-sities of the codebook. The mixture weights of the target lan-guage are modelled as convex combina...
The goal of our current research is to be able to separate a few audio sources from the signals of two microphones, us- ing a separate recording of each player clapping their hands. The separation is performed in the frequency domain, where speech and music signals are mostly sparse. Being under- determined, the separation is performed in two steps...
![Table 2 : Multi-lingual test results, WER [%].](profile/F-Diehl/publication/221482139/figure/tbl2/AS:647927919812614@1531489462486/Multi-lingual-test-results-WER_Q320.jpg)
In this paper we present a method for defining the question set for the induction of acoustic phonetic decision trees. The method is data driven resulting in a continuous feature space in contrast to the usual categorical one. We apply the features to a multi- lingual speech recognition task, outperforming consistently the standard method using IPA...
In this paper, the use of a specific metric as a feature selection step is investigated. The feature selection step tries to model the correlation among adjacent feature vectors and the variability of the speech. We propose a new procedure which performs the feature selection in two steps. The first step takes into account the temporal correlation...
A continuous speech recognition system (called RAMSES) has been built based on the demisyllable as phonetic unit and tools from connected speech recognition. Speech is parameterized by band-pass lifted LPC-cepstra and demisyllables are represented by hidden Markov models (HMM). In this paper, the application of this system to recognize integer numb...
In the paper we present the theoretical development of the normalized backpropagation, and we compare it with other algorithms that have been presented in the literature.1
A procedure for feature selection in isolated word recognition is discussed. The feature selection is performed in two steps. The first step takes into account the temporal correlation among feature vectors in order to obtain a transformation matrix which projects the initial template of N feature vectors to a new space where they are uncorrelated....
One of the most popular algorithms for connected word (or subword phonetic unit) recognition is the one-stage dynamic programming algorithm. In its available formulation, this algorithm is not designed to provide multiple hypothesis; such a limitation is currently becoming a drawback, since the need of alternative recognitions in the systems presen...
