Context. Data gaps are ubiquitous in spectral irradiance data, and yet, little effort has been put into finding robust methods for filling them. Aims. We introduce a data-adaptive and nonparametric method that allows us to fill data gaps in multi-wavelength or in multichannel records. Methods. This method, which is based on the iterative singular value decomposition, uses the coherency between simultaneous measurements at different wavelengths (or between different proxies) to fill the missing data in a self-consistent way. The interpolation is improved by handling different time scales separately. Results. Two major assets of this method are its simplicity, with few tuneable parameters, and its robustness. Two examples of missing data are given: one from solar EUV observations, and one from solar proxy data. The method is also appropriate for building a composite out of partly overlapping records.

The analysis of weak variations in the energetic particle flux, as detected by neutron or muon monitors, can often be considerably improved by analysing data from monitor networks and thereby exploiting the spatial coherence of the flux. We present a statistical framework for carrying out such an analysis and discuss its physical interpretation. Two other applications are also presented: filling data gaps and removing trends. This study focuses on the method and its various uses.