A study of stationarity in time series by using wavelet transform

By Amel Abdoullah Ahmed Dghais, M. Ismail. 2014

7 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. A. Ben Abbes
2. Beatriz Martínez
3. Yanfang Sang
4. Imed Riadh El Farah
5. Manel Rhif

In Management Science and Operations Research

1. A. Ben Abbes
2. Beatriz Martínez
3. Wei Bao
4. Jun Yue
5. Yanfang Sang
6. Imed Riadh El Farah
7. Manel Rhif
8. Yulei Rao

In Signal Processing

1. Yanfang Sang

Citations in the corpus

• A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017, background): “The mother wavelets and the father wavelets in the j - level can be formulated as[55]: Financial time series can be reconstructed by a series of projections on the mother and father wavelets with multilevel analysis indexed by k (cid:15) {0,1,2, . . . } and by j (cid:15) {0,1,2, . . .”
• Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review (2019, methodology): “In finance, the DWT, MODWT and XWT are the principal used types [52,59], where the main advantage is the ability to conserve time information.”