Predicting Rainfall in the Context of Rainfall Derivatives Using Genetic Programming

Rainfall is one of the most challenging variables to predict, as it exhibits very unique characteristics that do not exist in other time series data. Moreover, rainfall is a major component and is essential for applications that surround water resource planning. In particular, this paper is interested in the prediction of rainfall for rainfall derivatives. Currently in the rainfall derivatives literature, the process of predicting rainfall is dominated by statistical models, namely using a Markov-chain extended with rainfall prediction (MCRP). In this paper we outline a new methodology to be carried out by predicting rainfall with Genetic Programming (GP). This is the first time in the literature that GP is used within the context of rainfall derivatives. We have created a new tailored GP to this problem domain and we compare the performance of the GP and MCRP on 21 different data sets of cities across Europe and report the results. The goal is to see whether GP can outperform MCRP, which acts as a benchmark. Results indicate that in general GP significantly outperforms MCRP, which is the dominant approach in the literature

A robust linear regression based algorithm for automated evaluation of peptide identifications from shotgun proteomics by use of reversed-phase liquid chromatography retention time

<p>Abstract</p> <p>Background</p> <p>Rejection of false positive peptide matches in database searches of shotgun proteomic experimental data is highly desirable. Several methods have been developed to use the peptide retention time as to refine and improve peptide identifications from database search algorithms. This report describes the implementation of an automated approach to reduce false positives and validate peptide matches.</p> <p>Results</p> <p>A robust linear regression based algorithm was developed to automate the evaluation of peptide identifications obtained from shotgun proteomic experiments. The algorithm scores peptides based on their predicted and observed reversed-phase liquid chromatography retention times. The robust algorithm does not require internal or external peptide standards to train or calibrate the linear regression model used for peptide retention time prediction. The algorithm is generic and can be incorporated into any database search program to perform automated evaluation of the candidate peptide matches based on their retention times. It provides a statistical score for each peptide match based on its retention time.</p> <p>Conclusion</p> <p>Analysis of peptide matches where the retention time score was included resulted in a significant reduction of false positive matches with little effect on the number of true positives. Overall higher sensitivities and specificities were achieved for database searches carried out with MassMatrix, Mascot and X!Tandem after implementation of the retention time based score algorithm.</p

A Genetic Decomposition Algorithm for Predicting Rainfall within Financial Weather Derivatives

Regression problems provide some of the most challenging research opportunities, where the predictions of such domains are critical to a specific application. Problem domains that exhibit large variability and are of chaotic nature are the most challenging to predict. Rainfall being a prime example, as it exhibits very unique characteristics that do not exist in other time series data. Moreover, rainfall is essential for applications that surround financial securities such as rainfall derivatives. This paper is interested in creating a new methodology for increasing the predictive accuracy of rainfall within the problem domain of rainfall derivatives. Currently, the process of predicting rainfall within rainfall derivatives is dominated by statistical models, namely Markov-chain extended with rainfall prediction (MCRP). In this paper, we propose a novel algorithm for decomposing rainfall, which is a hybrid Genetic Programming/Genetic Algorithm (GP/GA) algorithm. Hence, the overall problem becomes easier to solve. We compare the performance of our hybrid GP/GA, against MCRP, Radial Basis Function and GP without decomposition. We aim to show the effectiveness that a decomposition algorithm can have on the problem domain. Results show that in general decomposition has a very positive effect by statistically outperforming GP without decomposition and MCRP

Feature Engineering for Improving Financial Derivatives-based Rainfall Prediction

Rainfall is one of the most challenging variables to predict, as it exhibits very unique characteristics that do not exist in other time series data. Moreover, rainfall is a major component and is essential for applications that surround water resource planning. In particular, this paper is interested in extending previous work carried out on the prediction of rainfall using Genetic Programming (GP) for rainfall derivatives. Currently in the rainfall derivatives literature, the process of predicting rainfall is dominated by statistical models, namely using a Markov-chain extended with rainfall prediction (MCRP). In this paper we further extend our new methodology by looking at the effect of feature engineering on the rainfall prediction process. Feature engineering will allow us to extract additional information from the data variables created. By incorporating feature engineering techniques we look to further tailor our GP to the problem domain and we compare the performance of the previous GP, which previously statistically outperformed MCRP, against our new GP using feature engineering on 21 different data sets of cities across Europe and report the results. The goal is to see whether GP can outperform its predecessor without extra features, which acts as a benchmark. Results indicate that in general GP using extra features significantly outperforms a GP without the use of extra features

Stochastic model genetic programming: Deriving pricing equations for rainfall weather derivatives

Rainfall derivatives are in their infancy since starting trading on the Chicago Mercantile Exchange (CME) in 2011. Being a relatively new class of financial instruments there is no generally recognised pricing framework used within the literature. In this paper, we propose a novel Genetic Programming (GP) algorithm for pricing contracts. Our novel algorithm, which is called Stochastic Model GP (SMGP), is able to generate and evolve stochastic equations of rainfall, which allows us to probabilistically transform rainfall predictions from the risky world to the risk-neutral world. In order to achieve this, SMGP's representation allows its individuals to comprise of two weighted parts, namely a seasonal component and an autoregressive component. To create the stochastic nature of an equation for each SMGP individual, we estimate the weights by using a probabilistic approach. We evaluate the models produced by SMGP in terms of rainfall predictive accuracy and in terms of pricing performance on 42 cities from Europe and the USA. We compare SMGP to 8 methods: its predecessor DGP, 5 well-known machine learning methods (M5 Rules, M5 Model trees, k-Nearest Neighbors, Support Vector Regression, Radial Basis Function), and two statistical methods, namely AutoRegressive Integrated Moving Average (ARIMA) and Monte Carlo Rainfall Prediction (MCRP). Results show that the proposed algorithm is able to statistically outperform all other algorithms

Tariff policy and sustainability of public transport in Curitiba/PR

This work discusses the concept of sustainability in the public transport system. Specifically, it seeks to discuss how the concept fits into the public transport fare policy. The working hypothesis is that the tariff policy is not an instrument of sustainable urban development, as the costs of the system are mostly covered by users. The methodological procedures are based on a bibliographic review of the concepts, explaining their dimensions and pointing out the absences and presences in the research object (Integrated Transport Network). As provisional conclusions of the research, it is advanced that sustainability is presented only as specific actions, such as the introduction of vehicles with sustainable technologies and the use of biodiesel, which represent an important step towards sustainable urban mobility; however, the social and economic dimensions are not fully addressed. Keywords: sustainability, sustainable urban mobilty, Curitiba’s public transport, sustainable urban developmentDiscute o conceito de sustentabilidade no sistema de transporte público. Especificamente, discute como o conceito se insere na política tarifária do transporte público coletivo. A hipótese de trabalho é que a política tarifária não é um instrumento de desenvolvimento urbano sustentável, pois os custos do sistema são cobertos em sua grande maioria pelos usuários. Os procedimentos metodológicos se baseiam em revisão bibliográfica dos conceitos, explicitando suas dimensões e apontando as ausências e presenças no objeto de pesquisa (Rede Integrada de Transporte). Como conclusões provisórias da pesquisa, adianta-se que a sustentabilidade apresenta-se através de ações pontuais, como a introdução de veículos com tecnologias sustentáveis e o uso de biodiesel, que representam um passo importante para mobilidade urbana sustentável; entretanto, as dimensões sociais e econômicas não são contempladas em sua plenitude. Palavras-chave: sustentabilidade, mobilidade urbana sustentável, transporte público de Curitiba, desenvolvimento urbano sustentável.Peer Reviewe

The Non-Canonical Hydroxylase Structure of YfcM Reveals a Metal Ion-Coordination Motif Required for EF-P Hydroxylation

EF-P is a bacterial tRNA-mimic protein, which accelerates the ribosome-catalyzed polymerization of poly-prolines. In Escherichia coli, EF-P is post-translationally modified on a conserved lysine residue. The post-translational modification is performed in a two-step reaction involving the addition of a β-lysine moiety and the subsequent hydroxylation, catalyzed by PoxA and YfcM, respectively. The β-lysine moiety was previously shown to enhance the rate of poly-proline synthesis, but the role of the hydroxylation is poorly understood. We solved the crystal structure of YfcM and performed functional analyses to determine the hydroxylation mechanism. In addition, YfcM appears to be structurally distinct from any other hydroxylase structures reported so far. The structure of YfcM is similar to that of the ribonuclease YbeY, even though they do not share sequence homology. Furthermore, YfcM has a metal ion-coordinating motif, similar to YbeY. The metal ion-coordinating motif of YfcM resembles a 2-His-1-carboxylate motif, which coordinates an Fe(II) ion and forms the catalytic site of non-heme iron enzymes. Our findings showed that the metal ion-coordinating motif of YfcM plays an essential role in the hydroxylation of the β-lysylated lysine residue of EF-P. Taken together, our results suggested the potential catalytic mechanism of hydroxylation by YfcM

Age Estimates for Globular Clusters in NGC 1399

We present high signal-to-noise Keck spectra for 10 globular clusters associated with the giant Fornax elliptical NGC 1399, and compare measured line indices with current stellar population models. Our data convincingly demonstrate, for the first time, that at least some of the clusters in a giant elliptical galaxy have super-solar abundance ratios, similar to the host galaxy. From Hbeta line-strengths the majority of clusters have ages of ~11 Gyrs (within 2sigma), which is similar to the luminosity-weighted stellar age of NGC 1399. Two of the clusters (which also reveal enhanced abundance ratios) show significantly higher Hbeta values than the others. It remains unclear whether this is due to young (~2 Gyr) ages, or extremely old (> 15 Gyr) ages with a warm blue horizontal branch. However a conflict with current cosmological parameters is avoided if the young age is favored. Either alternative indicates a complicated age distribution among the metal-rich clusters and sets interesting constraints on their chemical enrichment at late epochs.Comment: 5 pages, Latex, 3 figures, 1 table, accepted for publication in ApJ