The long-term memory prediction by multiscale decomposition

Soltani, Skander; Boichu, D.; Simard, Patrice; Canu, Stéphane

doi:10.1016/s0165-1684(00)00077-3

Cited by 45 publications

(13 citation statements)

References 13 publications

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“…Several approaches have been proposed for time-series prediction by the wavelet transform, based on a neural network (Bashir & El-Hawary, 2000;Zheng, Starck, Campbell, & Murtagh, 1999;Lotri˘c, 2004). In views of Zheng et al (1999) NON-CLASSICAL ALGORITHM FOR TIME SERIES PREDICTION 223 and Soltani, Boichu, Simard, and Canu (2000), the undecimated Haar transform was used.…”

Section: Literature Reviewmentioning

confidence: 99%

Non-classical Algorithm for Time Series Prediction of the Range of Economic Phenomena With Regard to the Interaction of Financial Market Indicators

2014

CBR

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The aim of the article is to present non-clasical copyrighted algorithm for prediction of time series, presenting macroeconomic indicators and stock market indices. The algorithm is based on artificial neural networks and multi-resolution analysis (the algorithm is based on Daubechies wavelet). However, the main feature of the algorithm, which gives a good quality of the forecasts, is all included in the series analysis division into, a few

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Section: Literature Reviewmentioning

confidence: 99%

Non-classical Algorithm for Time Series Prediction of the Range of Economic Phenomena With Regard to the Interaction of Financial Market Indicators

2014

CBR

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“…The forecasting method proposed by Wong et al (2003) relies on the decomposition of the time series using wavelets into three summands: trend, harmonic and irregular components. The method of Soltani et al (2000) exploits the de-correlating property of wavelets to forecast long-memory processes. Zheng et al (2000) combine wavelets and Kalman filtering by modeling wavelet coefficients as state variables for the Kalman filter.…”

Section: Wavelet Analysismentioning

confidence: 99%

Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis

Adamowski

2008

Journal of Hydrology

167

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“…Several approaches have been proposed for time-series prediction by the wavelet transform, based on a neural network in works of Bashir and El-Hawary (2000), Zheng, Starck, Campbell, and Murtagh (1999), and Lotri'c (2004). According to Zheng et al (1999) and Soltani, Boichu, Simard, and Canu (2000), the undecimated Haar transform was used.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Wavelets as Basis Functions in the Adaptation’s Methods: Author’s Model for Forecasting Short-Term

Hadaś-Dyduch¹

2016

CBR

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The aim of this article is to present author's application of wavelets to predict short-term macroeconomic indicators.Proposed to predict short-term time series (in particular for predicting macroeconomic indicators), proprietary model is based on wavelet analysis with Haar wavelets, Daubechies wavelets, and adaptive models; they are the trend crawling model and alignment exponential model. Adaptive models have been modified through the introduction of wavelet function and combined into a single forecast model. Obtained from conducted research results, it shows the model an effective instrument to predict the short-term.

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The long-term memory prediction by multiscale decomposition

Cited by 45 publications

References 13 publications

Non-classical Algorithm for Time Series Prediction of the Range of Economic Phenomena With Regard to the Interaction of Financial Market Indicators

Non-classical Algorithm for Time Series Prediction of the Range of Economic Phenomena With Regard to the Interaction of Financial Market Indicators

Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis

Wavelets as Basis Functions in the Adaptation’s Methods: Author’s Model for Forecasting Short-Term

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