Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area. Global forecasting models that are trained across sets of time series have shown a huge potential in providing accurate forecasts compared with the traditional univariate forecasting models that work on isolated series. However, there are currently no comprehensive time series archives for forecasting that contain datasets of time series from similar sources available for the research community to evaluate the performance of new global forecasting algorithms over a wide variety of datasets. In this paper, we present such a comprehensive time series forecasting archive containing 20 publicly available time series datasets from varied domains, with different characteristics in terms of frequency, series lengths, and inclusion of missing values. We also characterise the datasets, and identify similarities and differences among them, by conducting a feature analysis. Furthermore, we present the performance of a set of standard baseline forecasting methods over all datasets across eight error metrics, for the benefit of researchers using the archive to benchmark their forecasting algorithms.

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Current definitions of acute kidney injury use a urine output threshold of less than 0.5 mL/kg/hr, which have not been validated in the modern era. We aimed to determine the prognostic importance of urine output within the first 24 hours of admission to the ICU and to evaluate for variance between different admission diagnoses.DESIGN: Retrospective cohort study. SETTING:One-hundred eighty-three ICUs throughout Australia and New Zealand from 2006 to 2016. PATIENTS:Patients greater than or equal to 16 years old who were admitted with curative intent who did not regularly receive dialysis. ICU readmissions during the same hospital admission and patients transferred from an external ICU were excluded. MEASUREMENTS AND MAIN RESULTS:One hundred and sixty-one thousand nine hundred forty patients were included with a mean urine output of 1.05 mL/kg/hr and an overall in-hospital mortality of 7.8%. A urine output less than 0.47 mL/kg/hr was associated with increased unadjusted in-hospital mortality, which varied with admission diagnosis. A machine learning model (extreme gradient boosting) was trained to predict in-hospital mortality and examine interactions between urine output and survival. Low urine output was most strongly associated with mortality in postoperative cardiovascular patients, nonoperative gastrointestinal admissions, nonoperative renal/genitourinary admissions, and patients with sepsis. CONCLUSIONS:Consistent with current definitions of acute kidney injury, a urine output threshold of less than 0.5 mL/kg/hr is modestly predictive of mortality in patients admitted to the ICU. The relative importance of urine output for predicting survival varies with admission diagnosis.

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Working towards active buildings that fully integrate efficient demand management with renewable energy sources and storage, energy efficiency is an important step, as building inefficiencies cause energy wastage and increase energy-related expenses. Currently, static thermal setpoints are typically used to maintain the inside temperature of a building at a comfortable level irrespective of its occupancy. This paper introduces a deep learning framework that trains across time series to forecast the temperatures of a future period directly where a particular room is unoccupied and optimises the setpoints of the room. To the best of our knowledge, this is the first study to use a state-of-the-art deep learning method trained across series to accurately predict temperatures for the subsequent optimal control of room setpoints. In contrast to traditional forecasting approaches that build isolated models to predict each series, our framework uses global recurrent neural network models that are trained with a set of relatively short temperature series, allowing the models to learn cross-series information. The predicted temperatures were then used to define the optimal thermal setpoints to be used inside the room during the unoccupied periods. We evaluate the prediction accuracy of our deep learning framework against a set of state-of-the-art forecasting models and can outperform those by a large margin. Furthermore, we analyse the usage of our deep learning framework to optimise the energy consumption of an air conditioning system in a real-world scenario using temperature data from a university lecture theatre. Based on simulations, we show that our proposed framework can lead to savings of approximately 20% and 15%, respectively, compared to the traditional temperature control model that does not use optimisation techniques and a programmable thermostat. INDEX TERMSDeep learning, energy optimisation, generative models, recurrent neural networks, temperature forecasting I. INTRODUCTION