Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

BACKGROUND: Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics. RESULTS: In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model. CONCLUSIONS: Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques

Tendencias recientes en el pronóstico de series de tiempo financieras usando máquinas de vectores de soporte

Resumen: El pronóstico de las series de tiempo financieras es un área de trabajo intensiva para investigadores y profesionales. En este estudio, analizamos 59 artículos y discutimos sobe el progreso en el análisis de series de tiempo financieras usando máquinas de vectores de soporte. Las principales conclusiones a las que llegamos son: (a) el pronóstico se hace con datos de frecuencia diaria y los estudios con otras frecuencias de tiempo son escasos; (b) la mayoría de los artículos están enfocados en mejorar el proceso de estimación de los parámetros o en el tratamiento previo de las series de tiempo; (c) la mayor parte de los artículos se concentran en el pronóstico de un índice financiero del mercado; (d) los casos experimentales están dispersos, lo que no hace posible comparar entre diferentes estudios.Abstract: Forecasting of financial time series is an intensive working area for researchers and practitioners. In this study, we analyze 59 articles and discuss the progress in financial time series analysis using support vector machines. Our main conclusions are: (a) forecasting is doing in a daily basis and studies in other time scales are scarce; (b) most of works are devoted to improve the parameter estimation process or to preprocessing the time series; (c) most of the work is concerned to forecast market financial index; (d) experimental cases are disperse and it is no possible to compare between different studiesMaestrí

Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction

Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. Nevertheless, existing literature showed that the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization parameter and kernel parameter, where this would greatly affect the generalization of LSSVM in prediction task. This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. On the other hand, the cmABC algorithm that incorporates conventional mutation addresses the over- fitting or under-fitting problem. The combination of lvABC and cmABC algorithm, which is later introduced as Enhanced Artificial Bee Colony–Least Squares Support Vector Machine (eABC-LSSVM), is realized in prediction of non renewable natural resources commodity price. Upon the completion of data collection and data pre processing, the eABC-LSSVM algorithm is designed and developed. The predictability of eABC-LSSVM is measured based on five statistical metrics which include Mean Absolute Percentage Error (MAPE), prediction accuracy, symmetric MAPE (sMAPE), Root Mean Square Percentage Error (RMSPE) and Theils’ U. Results showed that the eABC-LSSVM possess lower prediction error rate as compared to eight hybridization models of LSSVM and Evolutionary Computation (EC) algorithms. In addition, the proposed algorithm is compared to single prediction techniques, namely, Support Vector Machines (SVM) and Back Propagation Neural Network (BPNN). In general, the eABC-LSSVM produced more than 90% prediction accuracy. This indicates that the proposed eABC-LSSVM is capable of solving optimization problem, specifically in the prediction task. The eABC-LSSVM is hoped to be useful to investors and commodities traders in planning their investment and projecting their profit

Математичне моделювання і прогнозування нелінійних нестаціонарних процесів в економіці та фінансах

Магістерська дисертація: 103 с., 20 рис., 28 табл., 57 джерел, 1 додаток. Актуальність теми. Мінімізація фінансових ризиків та необхідність прийняття обґрунтованих управлінських рішень вимагають вдосконалення існуючих та пошуку нових підходів для реалізації високоякісних прогнозів. Зв’язок роботи з науковими програмами, планами, темами. Дисертаційна робота виконувалась згідно з планом науково-дослідних робіт кафедри математичних методів системного аналізу Національного технічного університету України «Київський політехнічний інститут імені Ігоря Сікорського». Мета дослідження. Підвищення адекватності математичних моделей нелінійних нестаціонарних процесів в економіці та фінансах і якості оцінок їх прогнозів за рахунок створення нових математичних моделей. Об’єкт дослідження. Нелінійні нестаціонарні процеси в економіці та фінансах, представлені статистичними даними стосовно їх розвитку. Предмет дослідження. Математичні моделі аналізу даних з метою моделювання і прогнозування розвитку нелінійних нестаціонарних процесів; множини критеріїв для аналізу адекватності моделей та оцінювання якості прогнозів. Методи дослідження. Використання регресійного підходу та методу групового урахування аргументів, метод найменших квадратів для оцінки параметрів моделі. Наукова новизна отриманих результатів. Розроблено програмний продукт для моделювання та прогнозування нелінійний нестаціонарних процесів. Апробація результатів дисертації. За матеріалами дисертаційної роботи опубліковано 1 тези конференції та подано до друку 1 статтю.The theme: Mathematical modeling and forecasting of nonlinear nonstationary processes in economics and finance. Master’s thesis: 103 p., 20 fig., 28 tab., 57 sources, 1 appendix. Topic Relevance. Minimizing financial risks and the need to make sound management decisions require improving existing and finding new approaches to implementing high-quality forecasts. Thesis connection to scientific programs, plans, and topics. The thesis was prepared according to the scientific research plan of the Department of Mathematical Methods of System Analysis of the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute. Research goal. Improving the adequacy of mathematical models of nonlinear nonstationary processes in economics and finance and the quality of estimates of their forecasts by creating new mathematical models. Object of research. Nonlinear non-stationary processes in economics and finance, represented by statistics on their development. Subject of research. Mathematical models of data analysis for the purpose of modeling and forecasting the development of nonlinear nonstationary processes; sets of criteria for analyzing the adequacy of models and assessing the quality of forecasts. Methods of research. Using a regression approach and the method of group consideration of arguments, the method of least squares to estimate the parameters of the model. Scientific contribution. A software product for modeling and forecasting of nonlinear nonstationary processes has been developed

Forecasting price movements using technical indicators: investigating the impact of varying input window length

The creation of a predictive system that correctly forecasts future changes of a stock price is crucial for investment management and algorithmic trading. The use of technical analysis for financial forecasting has been successfully employed by many researchers. Input window length is a time frame parameter required to be set when calculating many technical indicators. This study explores how the performance of the predictive system depends on a combination of a forecast horizon and an input window length for forecasting variable horizons. Technical indicators are used as input features for machine learning algorithms to forecast future directions of stock price movements. The dataset consists of ten years daily price time series for fifty stocks. The highest prediction performance is observed when the input window length is approximately equal to the forecast horizon. This novel pattern is studied using multiple performance metrics: prediction accuracy, winning rate, return per trade and Sharpe ratio

Applications of hybrid neural networks and genetic programming in financial forecasting

This thesis explores the utility of computational intelligent techniques and aims to contribute to the growing literature of hybrid neural networks and genetic programming applications in financial forecasting. The theoretical background and the description of the forecasting techniques are given in the first part of the thesis (chapters 1-3), while the contribution is provided through the last five self-contained chapters (chapters 4-8). Chapter 4 investigates the utility of the Psi Sigma neural network when applied to the task of forecasting and trading the Euro/Dollar exchange rate, while Kalman Filter estimation is tested in combining neural network forecasts. A time-varying leverage trading strategy based on volatility forecasts is also introduced. In chapter 5 three neural networks are used to forecast an exchange rate, while Kalman Filter, Genetic Programming and Support Vector Regression are implemented to provide stochastic and genetic forecast combinations. In addition, a hybrid leverage trading strategy tests if volatility forecasts and market shocks can be combined to boost the trading performance of the models. Chapter 6 presents a hybrid Genetic Algorithm – Support Vector Regression model for optimal parameter selection and feature subset combination. The model is applied to the task of forecasting and trading three euro exchange rates. The results of these chapters suggest that the stochastic and genetic neural network forecast combinations present superior forecasts and high profitability. In that way, more light is shed in the demanding issue of achieving statistical and trading efficiency in the foreign exchange markets. The focus of the next two chapters shifts from exchange rate forecasting to inflation and unemployment prediction through optimal macroeconomic variable selection. Chapter 7 focuses on forecasting the US inflation and unemployment, while chapter 8 presents the Rolling Genetic – Support Vector Regression model. The latter is applied to several forecasting exercises of inflation and unemployment of EMU members. Both chapters provide information on which set of macroeconomic indicators is found relevant to inflation and unemployment targeting on a monthly basis. The proposed models statistically outperform traditional ones. Hence, the voluminous literature, suggesting that non-linear time-varying approaches are more efficient and realistic in similar applications, is extended. From a technical point of view, these algorithms are superior to non-adaptive algorithms; avoid time consuming optimization approaches and efficiently cope with dimensionality and data-snooping issues

Integrating supercapacitors into a hybrid energy system to reduce overall costs using the genetic algorithm (GA) and support vector machine (SVM)

This research deals with optimising a supercapacitor-battery hybrid energy storage system (SB-HESS) to reduce the implementation cost for solar energy applications using the Genetic Algorithm (GA) and the Support Vector Machine (SVM). The integration of a supercapacitor into a battery energy storage system for solar applications is proven to prolong the battery lifespan. Furthermore, the reliability of the system was optimised using a GA within the Taguchi technique in the supercapacitor fabrication process. This is important to reduce the spread in tolerance of supercapacitors values (i.e. capacitance and Equivalent Series Resistance (ESR)) which affect system performance. One of the more important results obtained in this project is the net present cost (NPC) of the Supercapacitor-battery hybrid energy storage system is 7.51% lower than the conventional battery only system over a 20-years project lifetime. This NPC takes into account of components initial capital cost, replacement cost, maintenance and operational cost. The number of batteries is reduced from 40 (conventional – battery only system) to 24 (SB-HESS) with the inclusion of supercapacitors in the system. This leads to reduction cost in the implemented hybrid energy storage system. A greener renewable energy system is achievable as the number of battery is reduced significantly. An optimised combination of the number of components for renewable energy system is also found. The number of batteries is sized, based on the average power output instead of catering to the peak power burst as in a conventional battery only system. This allows for the reduction in the number of batteries as the peak power is catered for by the presence of the supercapacitor. Subsequent efforts have been focused on the energy management system which is coupled with a supervised learning machine – SVM, switches and sensors are used to forecast the load demand beforehand. This load predictive-energy management system is implemented on a lab-scaled hybrid energy storage system prototype. Results obtained also show that this load predictive system allows for accurate load classification and prediction. The supercapacitor in the hybrid energy storage system is able to switch on to cater for peak power without delay. This is crucial in maintaining an optimised battery depth-of-discharge (DOD) in order to reduce the rate of battery damage thru a degradation mechanism which is caused from particular stress factors (especially sulphation on the battery electrode and electrolyte stratification)

Integrating supercapacitors into a hybrid energy system to reduce overall costs using the genetic algorithm (GA) and support vector machine (SVM)

This research deals with optimising a supercapacitor-battery hybrid energy storage system (SB-HESS) to reduce the implementation cost for solar energy applications using the Genetic Algorithm (GA) and the Support Vector Machine (SVM). The integration of a supercapacitor into a battery energy storage system for solar applications is proven to prolong the battery lifespan. Furthermore, the reliability of the system was optimised using a GA within the Taguchi technique in the supercapacitor fabrication process. This is important to reduce the spread in tolerance of supercapacitors values (i.e. capacitance and Equivalent Series Resistance (ESR)) which affect system performance. One of the more important results obtained in this project is the net present cost (NPC) of the Supercapacitor-battery hybrid energy storage system is 7.51% lower than the conventional battery only system over a 20-years project lifetime. This NPC takes into account of components initial capital cost, replacement cost, maintenance and operational cost. The number of batteries is reduced from 40 (conventional – battery only system) to 24 (SB-HESS) with the inclusion of supercapacitors in the system. This leads to reduction cost in the implemented hybrid energy storage system. A greener renewable energy system is achievable as the number of battery is reduced significantly. An optimised combination of the number of components for renewable energy system is also found. The number of batteries is sized, based on the average power output instead of catering to the peak power burst as in a conventional battery only system. This allows for the reduction in the number of batteries as the peak power is catered for by the presence of the supercapacitor. Subsequent efforts have been focused on the energy management system which is coupled with a supervised learning machine – SVM, switches and sensors are used to forecast the load demand beforehand. This load predictive-energy management system is implemented on a lab-scaled hybrid energy storage system prototype. Results obtained also show that this load predictive system allows for accurate load classification and prediction. The supercapacitor in the hybrid energy storage system is able to switch on to cater for peak power without delay. This is crucial in maintaining an optimised battery depth-of-discharge (DOD) in order to reduce the rate of battery damage thru a degradation mechanism which is caused from particular stress factors (especially sulphation on the battery electrode and electrolyte stratification)