A time series classifier

A time series is a sequence of data measured at successive time intervals. Time series analysis refers to all of the methods employed to understand such data, either with the purpose of explaining the underlying system producing the data or to try to predict future data points in the time series...An evolutionary algorithm is a non-deterministic method of searching a solution space, and modeled after biological evolutionary processes. A learning classifier system (LCS) is a form of evolutionary algorithm that operates on a population of mapping rules. We introduce the time series classifier TSC, a new type of LCS that allows for the modeling and prediction of time series data, derived from Wilson\u27s XCSR, an LCS designed for use with real-valued inputs. Our method works by modifying the makeup of the rules in the LCS so that they are suitable for use on a time series...We tested TSC on real-world historical stock data --Abstract, page iii

Estrategia de búsqueda de dispositivos basada en el historial de conexiones utilizando redes neuronales

La movilidad es una de las principales características de las redes de comunicación actuales y produce cambios en su estructura, que en muchas ocasiones no son advertidos por la totalidad de los dispositivos que la conforman, principalmente por la distancia entre los dispositivos y el rango de transmisión. Por ello, la comunicación entre dos dispositivos se convierte en un problema de enrutamiento, el cual se define como la búsqueda de trayectorias mediante una adecuada estrategia. Para abordar esta situación, planteamos una estrategia de búsqueda basada en el historial de onexiones del dispositivo móvil. Por naturaleza, una persona tiende a exhibir comportamientos repetitivos, por lo que, observando estos patrones conductuales podremos, con cierta certeza, ubicarlo en un espacio y tiempo específico. Tomando en consideración lo anterior, si utilizamos el historial de conexiones de un dispositivo y mediante las apropiadas herramientas stocásticas, se podría lograr una visión de la estructura de la red y de esta forma predecir la secuencia de dispositivos que formarían la trayectoria para la omunicación entre dos dispositivos. Utilizaremos como herramienta de predicción de secuencias redes neuronales y analizaremos su contribución en el diseño de un algoritmo de búsqueda de dispositivos móviles.Postprint (published version

An investigation into the use of neural networks for the prediction of the stock exchange of Thailand

Stock markets are affected by many interrelated factors such as economics and politics at both national and international levels. Predicting stock indices and determining the set of relevant factors for making accurate predictions are complicated tasks. Neural networks are one of the popular approaches used for research on stock market forecast. This study developed neural networks to predict the movement direction of the next trading day of the Stock Exchange of Thailand (SET) index. The SET has yet to be studied extensively and research focused on the SET will contribute to understanding its unique characteristics and will lead to identifying relevant information to assist investment in this stock market. Experiments were carried out to determine the best network architecture, training method, and input data to use for this task. With regards network architecture, feedforward networks with three layers were used - an input layer, a hidden layer and an output layer - and networks with different numbers of nodes in the hidden layers were tested and compared. With regards training method, neural networks were trained with back-propagation and with genetic algorithms. With regards input data, three set of inputs, namely internal indicators, external indicators and a combination of both were used. The internal indicators are based on calculations derived from the SET while the external indicators are deemed to be factors beyond the control of the Thailand such as the Down Jones Index

Portfolio management using computational intelligence approaches. Forecasting and Optimising the Stock Returns and Stock Volatilities with Fuzzy Logic, Neural Network and Evolutionary Algorithms.

Portfolio optimisation has a number of constraints resulting from some practical matters and regulations. The closed-form mathematical solution of portfolio optimisation problems usually cannot include these constraints. Exhaustive search to reach the exact solution can take prohibitive amount of computational time. Portfolio optimisation models are also usually impaired by the estimation error problem caused by lack of ability to predict the future accurately. A number of Multi-Objective Genetic Algorithms are proposed to solve the problem with two objectives subject to cardinality constraints, floor constraints and round-lot constraints. Fuzzy logic is incorporated into the Vector Evaluated Genetic Algorithm (VEGA) to but solutions tend to cluster around a few points. Strength Pareto Evolutionary Algorithm 2 (SPEA2) gives solutions which are evenly distributed portfolio along the effective front while MOGA is more time efficient. An Evolutionary Artificial Neural Network (EANN) is proposed. It automatically evolves the ANN¿s initial values and structures hidden nodes and layers. The EANN gives a better performance in stock return forecasts in comparison with those of Ordinary Least Square Estimation and of Back Propagation and Elman Recurrent ANNs. Adaptation algorithms for selecting a pair of forecasting models, which are based on fuzzy logic-like rules, are proposed to select best models given an economic scenario. Their predictive performances are better than those of the comparing forecasting models. MOGA and SPEA2 are modified to include a third objective to handle model risk and are evaluated and tested for their performances. The result shows that they perform better than those without the third objective