A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting
G Papacharalampous, H Tyralis - Frontiers in Water, 2022 - frontiersin.org
Probabilistic forecasting is receiving growing attention nowadays in a variety of applied
fields, including hydrology. Several machine learning concepts and methods are notably …
fields, including hydrology. Several machine learning concepts and methods are notably …
An innovative interpretable combined learning model for wind speed forecasting
P Du, D Yang, Y Li, J Wang - Applied Energy, 2024 - Elsevier
Wind energy is taken as one of the most potential green energy sources, whose accurate
and stable prediction is important to improve the efficiency of wind turbines as well as to …
and stable prediction is important to improve the efficiency of wind turbines as well as to …
Operational Research: methods and applications
Abstract Throughout its history, Operational Research has evolved to include methods,
models and algorithms that have been applied to a wide range of contexts. This …
models and algorithms that have been applied to a wide range of contexts. This …
Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic
Abstract Security Information and Event Management (SIEM) technologies play an important
role in the architecture of modern cyber protection tools. One of the main scenarios for the …
role in the architecture of modern cyber protection tools. One of the main scenarios for the …
Bayesian forecasting in economics and finance: A modern review
The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …
Forecast combination-based forecast reconciliation: Insights and extensions
T Di Fonzo, D Girolimetto - International Journal of Forecasting, 2022 - Elsevier
In this paper, we build upon a recently proposed forecast combination-based approach to
the reconciliation of a simple hierarchy (Hollyman R., Petropoulos F., Tipping ME …
the reconciliation of a simple hierarchy (Hollyman R., Petropoulos F., Tipping ME …
Forecasting pine sawtimber stumpage prices: A comparison between a time series hybrid model and an artificial neural network
S Lamichhane, B Mei, J Siry - Forest Policy and Economics, 2023 - Elsevier
We conducted a comparative analysis of the predictive ability of classical econometric
models and artificial neural networks (ANNs) for pine sawtimber stumpage prices across 22 …
models and artificial neural networks (ANNs) for pine sawtimber stumpage prices across 22 …
Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?
Probabilistic price forecasting has recently gained attention in power trading because
decisions based on such predictions can yield significantly higher profits than those made …
decisions based on such predictions can yield significantly higher profits than those made …
Ensemble learning for blending gridded satellite and gauge-measured precipitation data
Regression algorithms are regularly used for improving the accuracy of satellite precipitation
products. In this context, satellite precipitation and topography data are the predictor …
products. In this context, satellite precipitation and topography data are the predictor …