Is Skrei a Historical Norwegian Figure? The Nomadic Symbiosis of Fish and Humans in the Lofoten Islands

This paper draws from short ethnographic fieldwork and collected oral histories in the Lofoten Islands in Northern Norway in 2019. In this paper I follow “skrei”, the Norwegian codfish (Gadus morhua). I explore what I call the “nomadic symbiosis” of islanders and skrei via their diachronic entanglements, as these appear in historical and present narratives, in changing ideas around economic development and progress, but also in the changes in the physical and political landscapes. These moments of connection, all challenge human-centric views arguing for skrei’s agency in cuisine-making, but also vis-à-vis identity-making, as skrei became recognized conjuring a newfound sense of belonging and becoming part of an imagined community within the Lofoten islands and beyond. I argue that these meaningful interactions create worlds that decenter human agency and revisit the notion of cuisine and nation-building processes as truly multispecies entanglements

A review of probabilistic forecasting and prediction with machine learning

Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Although applications of probabilistic prediction and forecasting with machine learning models in academia and industry are becoming more frequent, related concepts and methods have not been formalized and structured under a holistic view of the entire field. Here, we review the topic of predictive uncertainty estimation with machine learning algorithms, as well as the related metrics (consistent scoring functions and proper scoring rules) for assessing probabilistic predictions. The review covers a time period spanning from the introduction of early statistical (linear regression and time series models, based on Bayesian statistics or quantile regression) to recent machine learning algorithms (including generalized additive models for location, scale and shape, random forests, boosting and deep learning algorithms) that are more flexible by nature. The review of the progress in the field, expedites our understanding on how to develop new algorithms tailored to users' needs, since the latest advancements are based on some fundamental concepts applied to more complex algorithms. We conclude by classifying the material and discussing challenges that are becoming a hot topic of research.Comment: 83 pages, 5 figure

Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow

We assess the performance of the recently introduced Prophet model in multi-step ahead forecasting of monthly streamflow by using a large dataset. Our aim is to compare the results derived through two different approaches. The first approach uses past information about the time series to be forecasted only (standard approach), while the second approach uses exogenous predictor variables alongside with the use of the endogenous ones. The additional information used in the fitting and forecasting processes includes monthly precipitation and/or temperature time series, and their forecasts respectively. Specifically, the exploited exogenous (observed or forecasted) information considered at each time step exclusively concerns the time of interest. The algorithms based on the Prophet model are in total four. Their forecasts are also compared with those obtained using two classical algorithms and two benchmarks. The comparison is performed in terms of four metrics. The findings suggest that the compared approaches are equally useful.</p