Paper Title
Forecasting Financial Time Series with Spectrum Analysis

In Physics, spectrum analysis models electrical, audio or seismic signals; in this paper, it is applied to financial time series forecasting. Any time series of financial assets may be decomposed in 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 that are 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. Keywords - Derivatives pricing; spectrum analysis, wavelet analysis.