Wavelet Denoising Based Multivariate Polynomial For Anchovy Catches Forecasting
نویسندگان
چکیده
In this paprer, a multivariate polynomial (MP) combined with denoising techniques is proposed to forecast 1-month ahead monthly anchovy catches in the north area of Chile. The anchovy catches data is denoised by using discrete stationary wavelet transform and then appropriate is used as inputs to the MP. The MP’s parameters are estimated using the penalized least square (LS) method and the performance evaluation of the proposed forecaster showed that a 98% of the explained variance was captured with a reduced parsimony.
منابع مشابه
Fejer-Korovkin Wavelet Based MIMO Model For Multi-step-ahead Forecasting of Monthly Fishes Catches
This paper proposes a Multiples Input-Multiples Ouput Autoregressive (MIMO-AR) model based on two stages to improve monthly anchovy catches forecasting of the coastal zone of Chile for periods from January 1958 to December 2011. In the first stage, the stationary wavelet transform (SWT) based on Fejer-Korovkin (FK) wavelet filter is used to separate the raw time series into a high frequency (HF...
متن کاملHaar Wavelet Neural Network for Multi-step-ahead Anchovy Catches Forecasting
This paper proposes a hybrid multi-step-ahead forecasting model based on two stages to improve pelagic fish-catch time-series modeling. In the first stage, the Fourier power spectrum is used to analyze variations within a time series at multiple periodicities, while the stationary wavelet transform is used to extract a high frequency (HF) component of annual periodicity and a low frequency (LF)...
متن کاملRBF Network Combined With Wavelet Denoising for Sardine Catches Forecasting
This paper deals with time series of monthly sardines catches in the north area of Chile. The proposed method combines radial basis function neural network (RBFNN) with wavelet denoising algorithm. Wavelet denoising is based on stationary wavelet transform with hard thresholding rule and the RBFNN architecture is composed of linear and nonlinear weights, which are estimated by using the separab...
متن کاملStatistical Wavelet-based Image Denoising using Scale Mixture of Normal Distributions with Adaptive Parameter Estimation
Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wa...
متن کاملComparative Analysis of Image Denoising Methods Based on Wavelet Transform and Threshold Functions
There are many unavoidable noise interferences in image acquisition and transmission. To make it better for subsequent processing, the noise in the image should be removed in advance. There are many kinds of image noises, mainly including salt and pepper noise and Gaussian noise. This paper focuses on the research of the Gaussian noise removal. It introduces many wavelet threshold denoising alg...
متن کامل