arXiv research

SPADE improves demand forecasting accuracy by 4.5% for post-promotion periods.

Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.

We design a dispatch system to improve the peak service quality of video on demand (VOD). Our system predicts the hot videos during the peak hours of the next day based on the historical requests, and dispatches to the content delivery networks (CDNs) at the previous off-peak time. In order to scale to billions of vide…

Study on-chain peak shaving to reduce Ethereum transaction costs.

Paper proposes a network framework for prosumers to manage peak loads in Iran.

Prob-GNN quantifies travel demand uncertainty with deep learning.

We present a comprehensive study of utility function of the minority game in its efficient regime. We develop an effective description of state of the game. For the payoff function $g(x)=\sgn (x)$ we explicitly represent the game as the Markov process and prove the finitness of number of states. We also demonstrate bou…

Deep Learning is applied to energy markets to predict extreme loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that arise due to supply and demand fluctuations from intraday system constraints. We propose deep spatio-temporal models and extreme value theo…

Wireless traffic prediction is a fundamental enabler to proactive network optimisation in beyond 5G. Forecasting extreme demand spikes and troughs due to traffic mobility is essential to avoiding outages and improving energy efficiency. Current state-of-the-art deep learning forecasting methods predominantly focus on o…

Smart grid uses deep learning to optimize household energy use.

A robust method for decomposing spectral peaks robust to distortion and interference.

We find empirically a characteristic sharp peak-flat trough pattern in a large set of commodity prices. We argue that the sharp peak structure reflects an endogenous inter-market organization, and that peaks may be seen as local ``singularities'' resulting from imitation and herding. These findings impose a novel strin…

Joint peak detection is a central problem when comparing samples in genomic data analysis, but current algorithms for this task are unsupervised and limited to at most 2 sample types. We propose PeakSegJoint, a new constrained maximum likelihood segmentation model for any number of sample types. To select the number of…

Mass spectrometry (MS) is an important technique for chemical profiling which calculates for a sample a high dimensional histogram-like spectrum. A crucial step of MS data processing is the peak picking which selects peaks containing information about molecules with high concentrations which are of interest in an MS in…

Strategic valuation of efficient and well-timed network investments under uncertain electricity market environment has become increasingly challenging, because there generally exist multiple interacting options in these investments, and failing to systematically consider these options can lead to decisions that underva…

The paper explains two distinct peaks in generalization error for neural networks and simpler models, each governed by different factors.

The heuristic identification of peaks from noisy complex spectra often leads to misunderstanding of the physical and chemical properties of matter. In this paper, we propose a framework based on Bayesian inference, which enables us to separate multipeak spectra into single peaks statistically and consists of two steps.…

Bayesian framework integrates spectral deconvolution with expert reasoning for robust peak estimation.

FLOPART solves peak detection by creating accurate train and test set predictions.

The paper shows how the generalization curve can have multiple peaks, influenced by data and learning algorithm biases.

PEAKS selects key training examples incrementally based on prediction error and kernel similarity.

PEAK tests means of multiple data streams with sequential betting.

Study analyzes how discounts affect train ticket purchases and rescheduling in Switzerland.

Data analysis in high-dimensional spaces aims at obtaining a synthetic description of a data set, revealing its main structure and its salient features. We here introduce an approach providing this description in the form of a topography of the data, namely a human-readable chart of the probability density from which t…

Recent increases in basic food prices are severely impacting vulnerable populations worldwide. Proposed causes such as shortages of grain due to adverse weather, increasing meat consumption in China and India, conversion of corn to ethanol in the US, and investor speculation on commodity markets lead to widely differin…

LLMs learn peaked distributions slowly due to power-law losses.

Finite-time queue peaks in stochastic networks have logarithmic scaling after geometric thresholds.

Bayesian Quadrature improves ensembling for neural networks with dispersed likelihood peaks.

During a stock market peak the price of a given stock ($i$) jumps from an initial level $p_1(i)$ to a peak level $p_2(i)$ before falling back to a bottom level $p_3(i)$. The ratios $A(i) = p_2(i)/p_1(i)$ and $B(i)= p_3(i)/p_1(i)$ are referred to as the peak- and bottom-amplitude respectively. The paper show…

tsbootstrap handles time series uncertainty without assuming independence.

In this paper, the fractional order curvature equation $(-Δ)^γu = (1 + \varepsilon K(x))u^{\frac{N + 2γ}{N - 2γ}}$ in $\mathbb{R}^N$ is considered. Assuming $K(x)$ has two critical points satisfying certain local conditions, we prove the existence of two-peak solutions.

A nonparametric method for time series analysis extracts envelopes, detects peaks, and clusters data.

Populations of species in ecosystems are often constrained by availability of resources within their environment. In effect this means that a growth of one population, needs to be balanced by comparable reduction in populations of others. In neutral models of biodiversity all populations are assumed to change increment…

Traditionally in regression one minimizes the number of fitting parameters or uses smoothing/regularization to trade training (TE) and generalization error (GE). Driving TE to zero by increasing fitting degrees of freedom (dof) is expected to increase GE. However modern big-data approaches, including deep nets, seem to…

New algorithm tackles constrained Markov decision processes with peak constraints.

We win EVA2025 by estimating extreme precipitation events using Peaks Over Thresholds and martingale testing.

This paper explains why double descent sometimes occurs weakly or not at all from an optimization perspective.

Support Vector Data Description (SVDD) provides a useful approach to construct a description of multivariate data for single-class classification and outlier detection with various practical applications. Gaussian kernel used in SVDD formulation allows flexible data description defined by observations designated as sup…

A new clustering algorithm reduces density peaks clustering's computational complexity.

We study the dynamics of order flows around large intraday price changes using ultra-high-frequency data from the Shenzhen Stock Exchange. We find a significant reversal of price for both intraday price decreases and increases with a permanent price impact. The volatility, the volume of different types of orders, the b…

Improved peak detection in ChIP-seq data reduces over-dispersion.

New model improves volatility forecasting by reducing overestimation and underestimation.

Extracts important peaks from XRD spectra using Attention mechanism.

During a speculative episode the price of an item jumps from an initial level p_1 to a peak level p_2 before more or less returning to level p_1. The ratio p_2/p_1 is referred to as the amplitude A of the peak. This paper shows that for a given market the peak amplitude is a linear function of the logarithm of the pric…

Study on convergence rate of Bergman metrics on Kähler manifolds.

Efficient neural Bayes estimators for censored peaks-over-threshold models improve inference speed and accuracy.

Novel graph-based method detects R-peaks in noisy ECG signals without preprocessing.

Method identifies financial rogue waves close to their onset.