Stock

Demand forecasting is one of the main challenges for retailers and wholesalers in any industry. Proper demand forecasting gives business valuable information about potential profits and helps managers in taking targeted decisions on business growth strategies. Nowadays almost all organizations use different data sources or databases for nearly every aspect of their operations so that the knowledge on products on sale belongs to several independent views. The methodology described in this paper addresses the issue of product demand forecasting in fashion industry exploiting a multi-view learning approach. In particular, we show how the integration and connection among multiple views improves results accuracy. In real-life applications not all the views are usually available before a product is put on the market but the utility of a proper demand forecasting increases if the prediction is available before the product launch. We show that missing views can be reconstructed by means of common latent factors; in particular, this paper presents a learning procedure that describes the connection between different views. This connection allows data integration from multiple sources and can be extended to the special case of partial data representation. The nearest neighbors in the latent space play a special role for this process and for a general improvement of the forecast quality. We experimented the proposed methodology on real fashion retail sales showing that multi-view latent learning provides a system that is able to reconstruct satisfactorily non yet available views and can be used to predict the volumes of sales well before the goods are put on the market.

Aim  This study investigated the use of stable δ13C and δ18O isotopes in the sagittal otolith carbonate of narrow-barred Spanish mackerel, Scomberomorus commerson, as indicators of population structure across Australia.Location  Samples were collected from 25 locations extending from the lower west coast of Western Australia (30°), across northern Australian waters, and to the east coast of Australia (18°) covering a coastline length of approximately 9500 km, including samples from Indonesia.Methods  The stable δ13C and δ18O isotopes in the sagittal otolith carbonate of S. commerson were analysed using standard mass spectrometric techniques. The isotope ratios across northern Australian subregions were subjected to an agglomerative hierarchical cluster analysis to define subregions. Isotope ratios within each of the subregions were compared to assess population structure across Australia.Results  Cluster analysis separated samples into four subregions: central Western Australia, north Western Australia, northern Australia and the Gulf of Carpentaria and eastern Australia. Isotope signatures for fish from a number of sampling sites from across Australia and Indonesia were significantly different, indicating population separation. No significant differences were found in otolith isotope ratios between sampling times (no temporal variation).Main conclusions  Significant differences in the isotopic signatures of S. commerson demonstrate that there is unlikely to be any substantial movement of fish among these spatially discrete adult assemblages. The lack of temporal variation among otolith isotope ratios indicates that S. commerson populations do not undergo longshore spatial shifts in distribution during their life history. The temporal persistence of spatially explicit stable isotopic signatures indicates that, at these spatial scales, the population units sampled comprise functionally distinct management units or separate ‘stocks’ for many of the purposes of fisheries management. The spatial subdivision evident among populations of S. commerson across northern and western Australia indicates that it may be advantageous to consider S. commerson population dynamics and fisheries management from a metapopulation perspective (at least at the regional level).