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In the field of time series forecasting, the combination of linear and nonlinear models has been explored to improve the accuracy of predictions when compared with the performance of individual models. Traditional forecasting methods such as the autoregressive integrated moving average (ARIMA) and exponential smoothing (ETS) are employed to map linear patterns in a time series while neural networks (ANNs) and support vector machines (SVMs) map nonlinear patterns. A traditional hybrid ARIMA–ANN system works by performing linear relationships in the data, considering the residual error a nonlinear component which is mapped by the ANN. The nature of a time series can be taken into consideration before applying a model. This work employs both linear and nonlinear patterns of a time series based on its volatility. This is explored using a hybrid evolutionary system composed by a simple exponential smoothing filter, ARIMA and autoregressive (AR) linear models and a SVR model. Particle swarm optimization is employed to optimize the order of the AR model, SVR parameters and number of lags of the time series. Experimental results show that the evolutionary hybrid system presented promising results in the forecasting domain. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, the Extreme Learning Machine (ELM) has been hybridized with the Particle Swarm Optimization (PSO) and such hybridization is called PSO-ELM. In most of these hybridizations, the PSO uses the Global topology. However, other topologies were designed to improve the performance of the PSO. In the literature, it is well known that the performance of the PSO depends on its topology, and there is not a best topology for all problems. Thus, in this paper, we investigate the effect of eight PSO topologies on performance of the PSO-ELM. The results showed empirically that the Global topology was more promising than all other topologies in optimizing the PSO-ELM according to the root mean squared error (RMSE) on the validation set in most of the evaluated datasets. However, no correlation was detected between this good performance on the RMSE and the testing accuracy. [Copyright &y& Elsevier]

Abstract: The use of artificial neural networks implies considerable time spent choosing a set of parameters that contribute toward improving the final performance. Initial weights, the amount of hidden nodes and layers, training algorithm rates and transfer functions are normally selected through a manual process of trial-and-error that often fails to find the best possible set of neural network parameters for a specific problem. This paper proposes an automatic search methodology for the optimization of the parameters and performance of neural networks relying on use of Evolution Strategies, Particle Swarm Optimization and concepts from Genetic Algorithms corresponding to the hybrid and global search module. There is also a module that refers to local searches, including the well-known Multilayer Perceptrons, Back-propagation and the Levenberg–Marquardt training algorithms. The methodology proposed here performs the search using the aforementioned parameters in an attempt to optimize the networks and performance. Experiments were performed and the results proved the proposed method to be better than trial-and-error and other methods found in the literature. [Copyright &y& Elsevier]

We present here an original work that applies meta-learning approaches to select models for time-series forecasting. In our work, we investigated two meta-learning approaches, each one used in a different case study. Initially, we used a single machine learning algorithm to select among two models to forecast stationary time series (case study I). Following, we used the NOEMON approach, a more recent work in the meta-learning area, to rank three models used to forecast time series of the M3-Competition (case study II). The experiments performed in both case studies revealed encouraging results. [Copyright &y& Elsevier]

Researches in natural neuron dynamics have shown that phase transition and chaos provide optimal behaviour for information processing. In artificial neural models that behavior is expected also to maximize the neuron learnability. By employing an open quantum systems approach, we propose a new quantum information extraction method in the quantum RAM node dynamics where complex values are iterated. We experimentally show bifurcation and chaos emergence by varying their parameters. [ABSTRACT FROM AUTHOR]

Time series forecasting is an important research field in machine learning. Since the literature shows several techniques for the solution of this problem, combining outputs of different models is a simple and robust strategy. However, even when using combiners, the experimenter may face the following dilemma: which technique should one use to combine the individual predictors? Inspired by classification and pattern recognition algorithms, this work presents a dynamic selection method of forecast combiners. In the dynamic selection, each test pattern is submitted to a certain combiner according to a nearest neighbor rule. The proposed method was used to forecast eight time series with chaotic behavior in short and long term. In general, the dynamic selection presented satisfactory results for all datasets. [ABSTRACT FROM AUTHOR]

Algorithm selection is an important task in different domains of knowledge. Meta-learning treats this task by adopting a supervised learning strategy. Training examples in meta-learning (called meta-examples) are generated from experiments performed with a pool of candidate algorithms in a number of problems, usually collected from data repositories or synthetically generated. A meta-learner is then applied to acquire knowledge relating features of the problems and the best algorithms in terms of performance. In this paper, we address an important aspect in meta-learning which is to produce a significant number of relevant meta-examples. Generating a high quality set of meta-examples can be difficult due to the low availability of real datasets in some domains and the high computational cost of labelling the meta-examples. In the current work, we focus on the generation of meta-examples for meta-learning by combining: (1) a promising approach to generate new datasets (called datasetoids ) by manipulating existing ones; and (2) active learning methods to select the most relevant datasets previously generated. The datasetoids approach is adopted to augment the number of useful problem instances for meta-example construction. However not all generated problems are equally relevant. Active meta-learning then arises to select only the most informative instances to be labelled. Experiments were performed in different scenarios, algorithms for meta-learning and strategies to select datasets. Our experiments revealed that it is possible to reduce the computational cost of generating meta-examples, while maintaining a good meta-learning performance. [ABSTRACT FROM AUTHOR]

Training artificial neural networks requires a tedious empirical evaluation to determine a suitable neural network architecture. To avoid this empirical process several techniques have been proposed to automatise the architecture selection process. In this paper, we propose a method to perform parameter and architecture selection for a quantum weightless neural network (qWNN). The architecture selection is performed through the learning procedure of a qWNN with a learning algorithm that uses the principle of quantum superposition and a non-linear quantum operator. The main advantage of the proposed method is that it performs a global search in the space of qWNN architecture and parameters rather than a local search. [ABSTRACT FROM AUTHOR]

Abstract: A supervised learning algorithm for quantum neural networks (QNN) based on a novel quantum neuron node implemented as a very simple quantum circuit is proposed and investigated. In contrast to the QNN published in the literature, the proposed model can perform both quantum learning and simulate the classical models. This is partly due to the neural model used elsewhere which has weights and non-linear activations functions. Here a quantum weightless neural network model is proposed as a quantisation of the classical weightless neural networks (WNN). The theoretical and practical results on WNN can be inherited by these quantum weightless neural networks (qWNN). In the quantum learning algorithm proposed here patterns of the training set are presented concurrently in superposition. This superposition-based learning algorithm (SLA) has computational cost polynomial on the number of patterns in the training set. [Copyright &y& Elsevier]

Abstract: This paper presents approaches to interval-valued time series forecasting. The first and second approaches are based on the autoregressive (AR) and autoregressive integrated moving average (ARIMA) models, respectively. The third approach is based on an artificial neural network (ANN) model and the last is based on a hybrid methodology that combines both ARIMA and ANN models. Each approach fits, respectively, two models on the mid-point and range of the interval values assumed by the interval-valued time series in the learning set. The forecasting of the lower and upper bounds of the interval value of the time series is accomplished through a combination of forecasts from the mid-point and range of the interval values. The evaluation of the models presented is based on the estimation of the average behavior of the mean absolute error and mean squared error in the framework of a Monte Carlo experiment. The results demonstrate that the approaches are useful in forecasting alternatives for interval-valued time series and indicate that the hybrid model is an effective way to improve the forecasting accuracy achieved by any one of the models separately. [Copyright &y& Elsevier]

We present a quantum probabilistic associative memory using the inverse of quantum Fourier transform and Grover's algorithm to recover existing or similar patterns in the memory. The content of the memory is created using a generator of a superposition state representing a given set of patterns. We discuss the architecture of the proposed memory including the storing, recovering and processing of similarity tolerance of the input query. The associative memory can extrapolate and recover similar stored patterns. The system is unitary and runs in O (n) steps, where n is the number of qubits of the patterns. [ABSTRACT FROM AUTHOR]

In order to investigate the dynamics of a quantum weightless neuron node we feed its output back as input. Due to the fact that controlled operators used in the neuron circuit usually generate entanglement, we propose a mathematical method to extract the output at time t and build from that output the input at time t + 1 . As a result the time evolution is a real-valued nonlinear map with one real parameter. The dynamics orbits are plotted showing acute sensitivity to initial conditions clearly exhibiting nonlinearity by just looking at amplitude graphs. The fractal geometry and regions of convergence are discussed by their Julia Set images and a new measure for model comparison is put forward. [ABSTRACT FROM AUTHOR]