Aminghafari M, Poggi JM (2007) Forecasting time series using wavelets. Int JWavelets Multiresolution Inf Process 5(5):709–724Aminghafari, M., Poggi, J.-M. (2007): Forecasting time series using wavelets. In- ternational Journal of Wavelets, Multiresolution and Information Processing, ...
Ismail B, Ataulla, Mohammed Yunus, "Time Series Forecasting Using Undecimated Wavelets, Neural Networks and Genetic Algorithm", International Journal of Electronics and Computer Science Engineering (IJECSE ,ISSN:2277-1956), Vol. 01, No. 03, pp.1404-1415 (2012)....
Using wavelets for time series forecasting: Does it pay off? By means of wavelet transform a time series can be decomposed into a time dependent sum of frequency components. As a result we are able to capture seasona... S Schlüter,C Deuschle - 《Fau Discussion Papers in Economics》 被引...
This paper describes a system formed by a mixture of expert models (MEM) for time-series forecasting. We deal with several different competing models, such as partial least squares, K -nearest neighbours and carbon copy. The input space, after changing its base using the Haar wavelets transfor...
摘要: In this thesis, we propose an improved exchange rate forecasting model based on neural network, stationary wavelet transform and statistical time series analysis techniques. We compare the new model's performance with pure neural network forecasting model...
Aisyah Mohammed S., Aftar Abu Bakar M., Mohd Ariff N. “Volatility forecasting of financial time series using wavelet based exponential generalized autoregressive conditional heteroscedasticity model” Communications in Statistics - Theory and Methods, 49 (1) (2020), pp. 178-188 https://doi.org/...
Additionally, wavelet-primarily based methods permit robust and green adaptation of the prediction fashions to changing sensor records, for this reason adapting the overall performance of the forecasts to exclusive situations. All these homes make wavelet-based time series forecasting an appealing choice...
Keywords Time serie forecasting Discrete wavelet transform ARIMA model Artificial neural network Zhang's hybrid model View PDFReferences [1] G.P. Zhang Time series forecasting using a hybrid ARIMA and neural network model Neurocomputing, 50 (2003), pp. 159-175 View PDFView articleGoogle Scholar ...
timet.Forinstance,theinformationmaycontainthehistoricalvalues ofr t andtherandomvectorYthatdescribestheeconomicenvironmentunderwhichtheassetpriceisdetermined. Asaresult,correlationsbetweenthevariableofinterestanditspastvaluesbecomethefocusoflineartimeseries analysis,andarereferredtoasserialcorrelationsorautocorrelations...
Wavelet packet decomposition splits an input time series into approximation and detail components, and the decomposed time series are used as inputs to artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WPANN and WPANFIS models, respectively. The forecasting ...