Abstract
Time series classification is a supervised learning method maps the input to the output using historical data. The primary objective is to discover interesting patterns hidden in the data. For the purpose of solving time series classification problems used the multi-layered perceptrons Artificial Neural Networks (ANN). The weights in the ANN are modified to provide the output values of the net, which are much closer to the values of the preferred output. For this reason, several algorithms had been proposed to train the parameters of the neural network for time series classification problems. This study attempts to hybrid the Firefly Algorithm (FA) with the ANN in order to minimize the error rate of classification (coded as FA-ANN). The FA is employed to optimize the weights of the ANN model based on the processes. The proposed FA-ANN algorithm was tested on 6 benchmark UCR time series data sets. The experimental results have revealed that the proposed FA-ANN can effectively solve time series classification problems.
Highlights
Time series data are produced, continuous and are processed within a wide range of application domains in different fields such as economics, engineering, science, medical and sociology
We investigate the search capability of the Firefly Algorithm (FA) in finding optimal values for the weights of the Artificial Neural Networks (ANN)
The FA is employed to optimise the weights of the ANN model, denoted as FA-ANN, to Firefly algorithm with Artificial Neural Networks (ANN): Rumelhart et al (1986) have proposed the multi-layered perceptrons (Artificial Neural Networks) for the purpose of solving time series classification problems
Summary
Time series data are produced, continuous and are processed within a wide range of application domains in different fields such as economics, engineering, science, medical and sociology. The popularity of time series data has been growing rapidly. This type of data occurs every second, every minute, hourly and daily in several applications. In the time series classification problem there are labels for each time point. Given a new time series, the task is to label all time points This is known as sequential supervised learning and is related to prediction (Fu, 2011). We investigate the search capability of the Firefly Algorithm (FA) in finding optimal values for the weights of the ANN To our knowledge, this is the first attempt where FA is experimented with ANN for time series classification problems. The ANN is employed first to obtain the initial solution and later the weights of the ANN will be adjusted by the FA in order to handle the time series data problem and minimize the error rate
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