Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 2
Abstract
Data originating from some specific fields, for in-stance tourist arrivals, may exhibit a high degree of fluctuations as well as non-linear characteristics due to time varying behaviors. This paper proposes a new hybrid method to perform prediction for such data. The proposed hybrid model of wavelet transform and long-short-term memory (LSTM) recurrent neural network (RNN) is able to capture non-linear attributes in tourist arrival time series. Firstly, data is decomposed into constitutive series through wavelet transform. The decomposition is expressed as a function of a combination of wavelet coefficients, which have different levels of resolution. Then, LSTM neural network is used to train and simulate the value at each level to find the bias vectors and weighting coefficients for the prediction value. A sliding windows model is employed to capture the time series nature of the data. An evaluation is conducted to compare the proposed model with other RNN algorithms, i.e., Elman RNN and Jordan RNN, as well as the combination of wavelet transform with each of them. The result shows that the proposed model has better performance in terms of training time than the original LSTM RNN, while the accuracy is better than the hybrid of wavelet-Elman and the hybrid of wavelet-Jordan.
Authors and Affiliations
Putu Sugiartawan, Reza Pulungan, Anny Kartika Sari
Keywords
Related Articles
- EP ID EP249202
- DOI 10.14569/IJACSA.2017.080243
- Views 98
- Downloads 0
Contextual Requirements for Mobile Native Applications
Mobile apps have found wide acceptance in today’s world which heavily depend on smart technology to access data over wide location. The apps are mostly of native type which can be used for accessing data even without the...
A Design of Pipelined Architecture for on-the-Fly Processing of Big Data Streams
Conventional processing infrastructures have been challenged by huge demand of stream-based applications. The industry responded by introducing traditional stream processing engines along-with emerged technologies. The o...
Denoising CT Images using wavelet transform
Image denoising is one of the most significant tasks especially in medical image processing, where the original images are of poor quality due the noises and artifacts introduces by the acquisition systems. In this paper...
Optimal Compression of Medical Images
In today’s healthcare system, medical images are playing a vital role in the diagnosis. The challenges arise to the hospital management systems (HMS) are to store and communicate the large volume of medical images genera...
Implementation of a Formal Software Requirements Ambiguity Prevention Tool
The success of the software engineering process depends heavily on clear unambiguous software requirements. Ambiguity refers to the possibility to understand a requirement in more than one way. Unfortunately, ambiguity i...