Harmony Search algorithm for optimal word size in symbolic time series representation

2011 ◽  
Author(s):  
Almahdi Mohammed Ahmed ◽  
Azuraliza Abu Bakar ◽  
Abdul Razak Hamdan
Keyword(s):  
Time Series ◽  
Search Algorithm ◽  
Harmony Search ◽  
Series Representation ◽  
Harmony Search Algorithm ◽  
Word Size

scholarly journals Bootstrapped Holt Method with Autoregressive Coefficients Based on Harmony Search Algorithm

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 839-850
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Ufuk Yolcu
Keyword(s):  
Time Series ◽  
Search Algorithm ◽  
Harmony Search ◽  
Exponential Smoothing ◽  
Harmony Search Algorithm ◽  
Time Varying ◽  
Smoothing Methods ◽  
Forecasting Methods ◽  
Smoothing Parameters ◽  
Exponential Smoothing Method

Exponential smoothing methods are one of the classical time series forecasting methods. It is well known that exponential smoothing methods are powerful forecasting methods. In these methods, exponential smoothing parameters are fixed on time, and they should be estimated with efficient optimization algorithms. According to the time series component, a suitable exponential smoothing method should be preferred. The Holt method can produce successful forecasting results for time series that have a trend. In this study, the Holt method is modified by using time-varying smoothing parameters instead of fixed on time. Smoothing parameters are obtained for each observation from first-order autoregressive models. The parameters of the autoregressive models are estimated by using a harmony search algorithm, and the forecasts are obtained with a subsampling bootstrap approach. The main contribution of the paper is to consider the time-varying smoothing parameters with autoregressive equations and use the bootstrap method in an exponential smoothing method. The real-world time series are used to show the forecasting performance of the proposed method.

scholarly journals Tuning Parameters in Deep Belief Networks for Time Series Prediction through Harmony Search

2021 ◽  
Vol 11 (4) ◽  
pp. 274-280
Author(s):  
Do Ngoc Luu ◽  
◽  
Nguyen Ngoc Phien ◽  
Duong Tuan Anh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Search Algorithm ◽  
Time Series Prediction ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Series Data ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Tuning Parameters

There have been several researches of applying Deep Belief Networks (DBNs) to predict time series data. Most of these works pointed out that DBNs can bring out better prediction accuracy than traditional Artificial Neural Networks. However, one of the main shortcomings of using DBNs in time series prediction concerns with the proper selection of their parameters. In this paper, we investigate the use of Harmony Search algorithm for determining the parameters of DBN in forecasting time series. Experimental results on several synthetic and real world time series datasets revealed that the DBN with parameters selected by Harmony Search performs better than the DBN with parameters selected by Particle Swarm Optimization (PSO) or random method in most of the tested datasets.

Optimal linear cooperation spectrum sensing method based on chaos harmony search algorithm

2013 ◽  
Vol 32 (9) ◽  
pp. 2412-2417
Author(s):  
Yue-hong LI ◽  
Pin WAN ◽  
Yong-hua WANG ◽  
Jian YANG ◽  
Qin DENG
Keyword(s):  
Spectrum Sensing ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Optimal Linear
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