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14 hours ago · Transformer-based forecasting models like Informer [49] utilize the sliding window method to construct the input dataset. An example demonstrating the use of a ...
19 hours ago · Diagnostic plots for standardized residuals of one endogenous variable. predict ([fh, X]). Forecast time series at future horizon.
13 hours ago · Abstract: Diffusion-based generative models have recently emerged as powerful solutions for high-quality synthesis in multiple domains.
13 hours ago · Abstract: Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in unconditional image generation.
14 hours ago · Most electrical load prediction models employ deep learning (DL) models based on time series to overcome the short-term uncertainty of key variables (Mokarram ...
9 hours ago · These models establish a relationship between input features, such as historical temperature data, geographical information, and temperature forecasts [3]. Time ...
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15 hours ago · Probabilistic models incorporating random variables have shown promise in accurately predicting material properties. Research has highlighted the importance ...
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14 hours ago · A probabilistic framework for lifelong test-time adaptation. In ... Evaluating prediction-time batch normalization for robustness under covariate shift.
Missing: series forecasting
7 hours ago · 1. The LSTM model effectively predicted time series for surge, heave, and pitch motions, as well as fairlead tensions under both operational and extreme wind ...
Fault Detection and Diagnosis as a Predictor of Energy Consumption in ...
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23 hours ago · In forecast-based fault detection techniques, any deviation from the baseline performance or healthy performance is deemed an anomaly with appropriate limits in ...