Unified architecture for multi-time-scale solar irradiance forecasting reduces RMSE.
problem Non-stationary solar irradiance variability increases grid operating costs.
method Recurrent Neural Networks (RNN) and Long-Short-Term Memory Networks (LSTMs) for multi-time-scale predictions.
result Unified architecture reduces RMSE by 71.5% compared to best-performing methods.
New method improves likelihood-free parameter estimation in complex models.
problem Estimating parameters in simulation-based models with unknown likelihood.
method Nested multi-time-scale stochastic approximation (NMTS) method.
result Eliminates bias and accelerates convergence in likelihood-free inference.
Enhances speech emotion recognition by adapting to varying time scales.
problem Robust emotion recognition from speech audio with temporal variations.
method Introduces multi-time-scale (MTS) convolutional layers to CNNs.
result MTS layers improve generalization, especially on smaller datasets.
New method improves parameter estimation in complex stochastic models.
problem Parameter calibration in stochastic models with unavailable analytical likelihood.
method Gradient-based simulated parameter estimation with multi-time scale stochastic approximation.
result Enhanced estimation accuracy and reduced computational costs.
Proposes a model to recommend products at the right time to meet user demands.
problem Maximizing product sales by recommending products at the right time to meet user demands.
method Integrates user interests and time-based demands into a Long-Short Demands-Aware Model (LSDM) using recurrent neural networks.
result Demonstrates the effectiveness of the LSDM in next-item recommendation on real-world commerce datasets.
The paper tackles energy management in buildings with PCM using dynamic programming.
problem Optimal scheduling of HVAC systems in buildings with PCM is challenging due to nonlinear and non-convex characteristics.
method The paper uses dynamic programming to address the nonlinear nature of PCM, incorporating macro actions and multi-time scale Markov decision processes to reduce computational burden.
result The proposed method demonstrates a computational speed-up of up to 12,900 times compared to direct DP application.
We propose a stochastic process for stock movements that, with just one source of Brownian noise, has an instantaneous volatility that rises from a type of statistical feedback across many time scales. This results in a stationary non-Gaussian process which captures many features observed in time series of real stock r…
Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare. The mean-variance function is one of the most widely used objective functions in risk management due to its simplicity and interpretability. Existing algorithms for mean-…
Paper proposes a network framework for prosumers to manage peak loads in Iran.
problem Balancing renewable prosumers' self-sufficiency with grid integration under uncertainty.
method Distributed contextual stochastic optimization (DCSO) framework with consensus-based sharing.
result Integration of prediction and optimization reduces peak loads and costs.
This paper studies the problem of parameter learning in probabilistic graphical models having latent variables, where the standard approach is the expectation maximization algorithm alternating expectation (E) and maximization (M) steps. However, both E and M steps are computationally intractable for high dimensional d…
Develops LSTM for predicting neuronal dynamics over long time-horizons.
problem Understanding and controlling complex brain behaviors.
method Long Short-Term Memory (LSTM) neural network architecture for multi-time step predictions.
result LSTM improves short time-horizon prediction accuracy and multi-time step predictions of neuronal dynamics.