Prediction Based on a Multiscale Decomposition

Renaud, Olivier; Starck, Jean-Luc; Murtagh, Fionn

doi:10.1142/s0219691303000153

Cited by 83 publications

(86 citation statements)

References 16 publications

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“…For example, suppose that stationary signal X = (X 1 ,X 2 ,…,X t ) and assume that value X t+1 will be forecasted. The basic idea is to use coefficients that are constructed from the decomposition, i.e., (Renaud et al, 2003): The first step that should be known is how many and which wavelet coefficients that should be used in each scale. Renaud et al (2003) introduced a process to calculate the forecast at time (t+1) th by using wavelet model as illustrated in Fig.…”

Section: Maximal Overlap Discrete Wavelet Transform (Modwt): One Of Mmentioning

confidence: 99%

“…Generally, there are two kinds of function that can be used in this input-output processing, such as linear and nonlinear functions. Renaud et al (2003) developed a linear wavelet model known as Multiscale Autoregressive (MAR) model. Moreover, Renaud et al (2003) also introduced the possibility of the nonlinear model use in inputoutput processing of wavelet model, especially FeedForward Neural Network (FFNN).…”

Section: Maximal Overlap Discrete Wavelet Transform (Modwt): One Of Mmentioning

confidence: 99%

“…Renaud et al (2003) developed a linear wavelet model known as Multiscale Autoregressive (MAR) model. Moreover, Renaud et al (2003) also introduced the possibility of the nonlinear model use in inputoutput processing of wavelet model, especially FeedForward Neural Network (FFNN). Furthermore the second model is known as Wavelet Neural Network (WNN) model.…”

Section: Maximal Overlap Discrete Wavelet Transform (Modwt): One Of Mmentioning

confidence: 99%

“…At the beginning, most wavelet research for time series analysis is focused on periodogram or scalogram analysis of periodicities and cycles evaluation (Priestley, 1996;Morettin, 1997;Gao, 1997;Percival and Walden, 2000). Bjorn (1995); Soltani et al (2000) and Renaud et al (2003) are some first researcher groups discussing wavelet for time series prediction based on autoregressive model. In this case, wavelet transformation gives good decomposition from a signal or time series, so that the structure can be evaluated by parametric or nonparametric models.…”

Section: Introductionmentioning

confidence: 99%

“…In time series forecasting cases, input used in WNN is wavelet coefficients in certain time and resolution. Recently, there are some articles about WNN for time series forecasting and filtering, such as Bashir and El-Hawary (2000); Renaud et al (2003); Murtagh et al (2004) and Chen et al (2006).…”

Section: Introductionmentioning

confidence: 99%

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Seasonal Time Series Data Forecasting by Using Neural Networks Multiscale Autoregressive Model

Taoufik¹

2010

American Journal of Applied Sciences

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Problem statement:The aim of this research was to study further some latest progress of wavelet transform for time series forecasting, particularly about Neural Networks Multiscale Autoregressive (NN-MAR). Approach: There were three main issues that be considered further in this research. The first was some properties of scale and wavelet coefficients from Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition, particularly at seasonal time series data. The second focused on the development of model building procedures of NN-MAR based on the properties of scale and wavelet coefficients. Then, the third was empirical study about the implementation of the proposed procedure and comparison study about the forecast accuracy of NN-MAR to other forecasting models. Results: The results showed that MODWT at seasonal time series data also has seasonal pattern for scale coefficient, whereas the wavelet coefficients are stationer. The result of model building procedure development yielded a new proposed procedure of NN-MAR model for seasonal time series forecasting. In general, this procedure accommodated input lags of scale and wavelet coefficients and other additional seasonal lags. In addition, the result showed that the proposed procedure works well for determining the best NN-MAR model for seasonal time series forecasting. Conclusion: The comparison study of forecast accuracy showed that the NN-MAR model yields better forecast than MAR and ARIMA models.

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Section: Maximal Overlap Discrete Wavelet Transform (Modwt): One Of Mmentioning

confidence: 99%

Section: Maximal Overlap Discrete Wavelet Transform (Modwt): One Of Mmentioning

confidence: 99%

Section: Maximal Overlap Discrete Wavelet Transform (Modwt): One Of Mmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Seasonal Time Series Data Forecasting by Using Neural Networks Multiscale Autoregressive Model

Taoufik¹

2010

American Journal of Applied Sciences

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A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

Ortega

Khashanah

2013

Journal of Forecasting

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We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.

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The wavelet scaling approach to forecasting: Verification on a large set of Noisy data

Bruzda

2019

Journal of Forecasting

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In the paper, we undertake a detailed empirical verification of wavelet scaling as a forecasting method through its application to a large set of noisy data. The method consists of two steps. In the first, the data are smoothed with the help of wavelet estimators of stochastic signals based on the idea of scaling, and, in the second, an AR(I)MA model is built on the estimated signal. This procedure is compared with some alternative approaches encompassing exponential smoothing, moving average, AR(I)MA and regularized AR models. Special attention is given to the ways of treating boundary regions in the wavelet signal estimation and to the use of biased, weakly biased and unbiased estimators of the wavelet variance. According to a collection of popular forecast accuracy measures, when applied to noisy time series with a high level of noise, wavelet scaling is able to outperform the other forecasting procedures, although this conclusion applies mainly to longer time series and not uniformly across all the examined accuracy measures.

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Prediction Based on a Multiscale Decomposition

Cited by 83 publications

References 16 publications

Seasonal Time Series Data Forecasting by Using Neural Networks Multiscale Autoregressive Model

Seasonal Time Series Data Forecasting by Using Neural Networks Multiscale Autoregressive Model

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

The wavelet scaling approach to forecasting: Verification on a large set of Noisy data

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