The versatility of spectrum analysis for forecasting financial time series
The versatility of the one‐dimensional discrete wavelet analysis combined with wavelet and Burg extensions for forecasting financial times series with distinctive properties is illustrated with market data. Any time series of financial assets may be decomposed into simpler signals called approximati...
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| Veröffentlicht in: | Journal of forecasting 2018-04, Vol.37 (3), p.327-339 |
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| container_end_page | 339 |
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| container_title | Journal of forecasting |
| container_volume | 37 |
| creator | Rostan, Pierre Rostan, Alexandra |
| description | The versatility of the one‐dimensional discrete wavelet analysis combined with wavelet and Burg extensions for forecasting financial times series with distinctive properties is illustrated with market data. Any time series of financial assets may be decomposed into simpler signals called approximations and details in the framework of the one‐dimensional discrete wavelet analysis. The simplified signals are recomposed after extension. The final output is the forecasted time series which is compared to observed data. Results show the pertinence of adding spectrum analysis to the battery of tools used by econometricians and quantitative analysts for the forecast of economic or financial time series. |
| doi_str_mv | 10.1002/for.2504 |
| format | Article |
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| fulltext | fulltext |
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| ispartof | Journal of forecasting, 2018-04, Vol.37 (3), p.327-339 |
| issn | 0277-6693 1099-131X |
| language | eng |
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| source | Wiley Online Library; Business Source Complete (EBSCO) |
| subjects | econometric modeling financial econometrics financial time series Forecasting forecasting and prediction methods mathematical and quantitative methods Spectrum analysis Time series Wavelet transforms |
| title | The versatility of spectrum analysis for forecasting financial time series |
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