Abstract

Abstract
This paper compares the accuracy of a set of 22 methods for short-term hotel demand forecasting for lead times up to 14 days ahead. Machine learning models are compared with methods ranging from seasonal naive to exponential smoothing methods for double seasonality. The machine learning methods considered include a new approach based on arbitrating, in which several forecasting models are dynamically combined to obtain predictions. Arbitrating is a metalearning approach that combines the output of experts according to predictions of the loss that they will incur. Particularly, the dynamic ensemble method is used. The methods were compared using a real time series of daily demand for a four-star hotel located in the south of Europe. The forecasting performance of those methods was assessed using three alternative accuracy measures. Results from extensive empirical experiments led us to conclude that machine learning methods outperform traditional hotel demand forecasting methods. We found that the use of machine learning models can reduce the root mean squared error up to 54% for a 1-day forecast horizon, and up to 45% for a 14-days forecast horizon, when compared with traditional exponential smoothing methods.

Abstract
Traditional historical data analytics is at risk in a world where volatility, uncertainty, complexity, and ambiguity are the new normal. While Streaming Machine Learning (SML) and Time-series Analytics (TSA) attack some aspects of the problem, we still need a comprehensive solution. SML trains models using fewer data and in a continuous/adaptive way relaxing the assumption that data points are identically distributed. TSA considers temporal dependence among data points, but it assumes identical distribution. Every Data Scientist fights this battle with ad-hoc solutions. In this paper, we claim that, due to the temporal dependence on the data, the existing solutions do not represent robust solutions to efficiently and automatically keep models relevant even when changes occur, and real-time processing is a must. We propose a novel and solid scientific foundation for Time-Evolving Analytics from this perspective. Such a framework aims to develop the logical, methodological, and algorithmic foundations for fast, scalable, and resilient analytics.

Abstract

Abstract
The aquaculture industry faces significant challenges related to sustainability, productivity, and fish welfare. Key issues include managing environmental conditions, disease, pests, and data integration from various sensors and monitoring systems. The BigFish project aims to address these challenges through advanced analytics and machine learning, focusing on three case studies in Atlantic salmon farms: predicting oxygen levels, reducing sea lice infestations, and improving data interaction and visualization. Predictive models for oxygen levels and sea lice infestation, as well as natural language interfaces for data visualization, demonstrate the potential for improved decision-making and management practices in aquaculture. Early results indicate the effectiveness of these approaches, highlighting the importance of data-driven solutions in enhancing industry sustainability and productivity. © 2024 IEEE.

Abstract
IntroductionHypoxia is defined as a critically low-oxygen condition of water, which, if prolonged, can be harmful to fish and many other aquatic species. In the context of ocean salmon fish farming, early detection of hypoxia events is critical for farm managers to mitigate these events to reduce fish stress, however in complex natural systems accurate forecasting tools are limited. The goal of this research is to use a machine learning approach to forecast oxygen concentration and predict hypoxia events in marine net-pen salmon farms.MethodsThe developed model is based on gradient boosting and works in two stages. First, we apply auto-regression to build a forecasting model that predicts oxygen concentration levels within a cage. We take a global forecasting approach by building a model using the historical data provided by sensors at several marine fish farms located in eastern Canada. Then, the forecasts are transformed into binary probabilities that indicate the likelihood of a low-oxygen event. We leverage the cumulative distribution function to compute these probabilities.Results and discussionWe tested our model in a case study that included several cages across 14 fish farms. The experiments suggest that the model can detect future hypoxic events with a commercially acceptable false alarm rate. The resulting probabilistic predictions and oxygen concentration forecasts can help salmon farmers to prioritize resources, and reduce harm to crops.