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José Francisco Lima, Fernanda Catarina Pereira, Arminda Manuela Gonçalves and Marco Costa
Linear models, seasonal autoregressive integrated moving average (SARIMA) models, and state-space models have been widely adopted to model and forecast economic data. While modeling using linear models and SARIMA models is well established in the literat...
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Sabrina De Nardi, Claudio Carnevale, Sara Raccagni and Lucia Sangiorgi
Models are a core element in performing local estimation of the climate change input. In this work, a novel approach to perform a fast downscaling of global temperature anomalies on a regional level is presented. The approach is based on a set of data-dr...
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Sumathi Kumaraswamy, Yomna Abdulla and Shrikant Krupasindhu Panigrahi
Recurrent stock market fall and rise sequel by COVID-19, rising global inflation, increase in Fed interest rates, the unprecedented meltdown of technology stocks, fear of trade wars, tightening of governments? fiscal policies call for a new trend in inte...
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Carla Sahori Seefoo Jarquin, Alessandro Gandelli, Francesco Grimaccia and Marco Mussetta
Understanding how, why and when energy consumption changes provides a tool for decision makers throughout the power networks. Thus, energy forecasting provides a great service. This research proposes a probabilistic approach to capture the five inherent ...
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Michael Wood, Emanuele Ogliari, Alfredo Nespoli, Travis Simpkins and Sonia Leva
Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and...
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