Journal ArticleDOI
Comparative performance of wavelet-based neural network approaches
Priyanka Anjoy,Ranjit Kumar Paul +1 more
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TLDR
This study discusses two wavelet-based neural network approaches envisaging monthly wholesale onion price of three markets, namely Bangalore, Hubli, and Solapur, and found to be highly proficient in denoising and capturing the inherent pattern of the series through a distinctive approach.Abstract:
An agriculture-dominated developing country like India has been always in need of efficient and reliable time series forecasting methodologies to describe various agricultural phenomenons, whereas agricultural price forecasting continue to be the challenging areas in this domain. The observed features of many temporal price data set constitute complex nonlinearity, and modeling these features often go beyond the capability of Box–Jenkins autoregressive integrated moving average methodology. Moreover, despite the popularity and sheer power of traditional neural network model, the empirical forecasting performance of this model has not been found satisfactory in all cases. To address the problem, wavelet-based modeling approach is recently upsurging. Present study discusses two wavelet-based neural network approaches envisaging monthly wholesale onion price of three markets, namely Bangalore, Hubli, and Solapur. Wavelet-based decomposition makes it possible to describe the useful pattern of the series from both global as well as local aspects and found to be highly proficient in denoising and capturing the inherent pattern of the series through a distinctive approach. Besides, wavelet method can also be used as a tool for function approximation. The improvement upon time-delay neural network also be made up to a great extent through using wavelet-based approaches as exhibited through proper empirical evidence.read more
Citations
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Journal ArticleDOI
GA-based design of optimal discrete wavelet filters for efficient wind speed forecasting
TL;DR: This paper suggests a new hybrid scheme WNN, based on discrete wavelet transform (DWT) combined with artificial neural network (ANN) for wind speed forecasting, which outperforms other conventional wavelet-based forecasting structures regarding the wind speed prediction precision.
Journal ArticleDOI
Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes
TL;DR: In this paper, three agricultural commodities (i.e., wheat, corn and soybean) spot prices were analyzed and three different regression models were used to estimate the time-varying parameters approach toward estimation of regression coefficients and dealing with model uncertainty.
Journal ArticleDOI
Forecasting Drought using Neural Network Approaches with Transformed Time Series Data
TL;DR: Drought is one of the important and costliest disaster all over the world and with the accelerated progress of climate change, its frequency of occurrence and negative impacts are rapidly increasing.
Journal ArticleDOI
Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices
Ranjit Kumar Paul,Sandip Garai +1 more
TL;DR: In this paper, a wavelet-based combination approach with artificial neural network (ANN) is proposed to forecast the monthly wholesale tomato price of three major markets in India, namely Ahmedabad, Burdwan and Madanapalli, where the decomposed and denoised components through wavelet transformation can be modeled using ANN to make waveletbased hybrid models and eventually, inverse wavelet transform is carried out to obtain the prediction of original series.
Journal ArticleDOI
Wavelets Based Artificial Neural Network Technique for Forecasting Agricultural Prices
Ranjit Kumar Paul,Sandip Garai +1 more
TL;DR: In this paper , a nonparametric wavelet technique has been applied for modeling monthly modal wholesale price of tomato for Burdwan market, West Bengal, India using two levels of decomposition i.e. 3 and 6.
References
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TL;DR: Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately.
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Diagnostic checking arma time series models using squared‐residual autocorrelations
A. I. McLeod,Wai Keung Li +1 more
TL;DR: In this article, the normalized squared-residual autocorrelations are shown to be asymptotically unit multivariate normal and the results of a simulation experiment confirming the small-sample validity of the proposed tests are reported.
Journal ArticleDOI
Threshold Autoregression, Limit Cycles and Cyclical Data
Howell Tong,K. S. Lim +1 more
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Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices
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