arXiv research

Paper tackles intermittent learning for energy-constrained machine learning tasks.

Paper develops deep models for forecasting intermittent demand.

Federated learning algorithm improves with intermittent client availability.

The study uses Gaussian Processes with Tweedie likelihood for forecasting intermittent time series.

Global models outperform local models in forecasting intermittent time series.

Unified framework for intermittent demand forecasting using renewal processes.

A new Bayesian model improves forecasting for intermittent demand.

Proposes a pricing agent using reinforcement learning to balance renewable energy demand.

NeuralPrefix fills in missing sensor data without additional training.

Maximum likelihood estimation applied to high-frequency data allows us to quantify intermittency in the fluctu- ations of asset prices. From time records as short as one month these methods permit extraction of a meaningful intermittency parameter λ characterising the degree of volatility clustering of asset prices. We…

We present a model of financial markets originally proposed for a turbulent flow, as a dynamic basis of its intermittent behavior. Time evolution of the price change is assumed to be described by Brownian motion in a power-law potential, where the `temperature' fluctuates slowly. The model generally yields a fat-tailed…

A new metric optimizes forecasts for lumpy, intermittent demand.

A new hierarchical forecasting method improves overall accuracy.

A new model captures multifractal volatility in stock returns.

A new model captures multifractal volatility in stock returns.

Factorial moments are convenient tools in nuclear physics to characterize the multiplicity distributions when phase-space resolution ($Δ$) becomes small. For uncorrelated particle production within $Δ$, Gaussian statistics holds and factorial moments $F_q$ are equal to unity for all orders $q$. Correlations between par…

In this paper we study the price dynamics in a simple model of financial markets with heterogeneous agents. We concentrate on how increases in the total number of active traders influences fluctuations of asset prices. We find that a curious route to chaos is observed when the total number of [active traders] increases…

Multiplicative random cascade model naturally reproduces the intermittency or multifractality, which is frequently shown among hierarchical complex systems such as turbulence and financial markets. As described herein, we investigate the validity of a multiplicative hierarchical random cascade model through an empirica…

We present a scalable and robust Bayesian inference method for linear state space models. The method is applied to demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson algorithm, reduced t…

Sharp heat kernel estimates on manifolds lead to solutions of the Parabolic Anderson model.

SurvSurf predicts first hitting times for intermittent events without monotonic violations.

Paper addresses state estimation in sensor networks with intermittent data.

Paper shows local SGD outperforms mini-batch SGD under certain conditions.

The Minority Game framework was recently generalized to account for the possibility that agents adapt not only through strategy selection but also by diversifying their response according to the kind of dynamical regime, or the risk, they perceive. Here we study the effects of this mechanism in different information st…

Successful implementation of California's Renewable Portfolio Standard (RPS) mandating 33 percent renewable energy generation by 2020 requires inclusion of a robust strategy to mitigate increased risk of energy deficits (blackouts) due to short time-scale (sub 1 hour) intermittencies in renewable energy sources. Of the…

Paper optimizes FL communication efficiency with stochastic optimization.

We consider several estimation and learning problems that networked agents face when making decisions given their uncertainty about an unknown variable. Our methods are designed to efficiently deal with heterogeneity in both size and quality of the observed data, as well as heterogeneity over time (intermittence). The …

A new method reduces communication in Federated Learning by pulling less often.

Study shows increased precipitation variability in Paris area over years.

ACI uses Bayesian data assimilation to trace causes from effects in complex systems.

In this paper, we show how simple logistic growth that was studied intensively during the last 200 years in many domains of science could be extended in a rather simple way and with these extensions is capable to produce a collection of behaviors widely observed in an enormous number of real-life systems in Economics, …

Study on collaborative vs. non-collaborative online and bandit convex optimization.

Minibatch SGD outperforms Local SGD in heterogeneous distributed learning.

The formation of price in a financial market is modelled as a chain of Ising spin with three fundamental figures of trading. We investigate the time behaviour of the model, and we compare the results with the real EURO/USD change rate. By using the test of local Poisson hypothesis, we show that this minimal model leads…

New method models complex dynamics using a base variable.

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…

This study investigates self-organizing dynamics in a stochastic exponential DAM model using Temporal Complexity.

We introduce a minimal Agent Based Model for financial markets to understand the nature and Self-Organization of the Stylized Facts. The model is minimal in the sense that we try to identify the essential ingredients to reproduce the main most important deviations of price time series from a Random Walk behavior. We fo…

SPECTRA improves probabilistic energy forecasting by separating trends and uncertainties.

We suggest a general oracle-based framework that captures different parallel stochastic optimization settings described by a dependency graph, and derive generic lower bounds in terms of this graph. We then use the framework and derive lower bounds for several specific parallel optimization settings, including delayed …

Paper proposes online learning for estimating AC network admittance matrix.

A new asynchronous method for vertical federated learning improves privacy and efficiency.

DynBRO learns robustly from dynamic Byzantine workers.

New method for IRL with missing data.

A new decentralized deep learning method reduces communication costs without sacrificing accuracy.

We study a phenomenological model for the continuous double auction, equivalent to two independent $M/M/1$ queues. The continuous double auction defines a continuous-time random walk for trade prices. The conditions for ergodicity of the auction are derived and, as a consequence, three possible regimes in the behavior …

Kaggle competitions offer valuable insights for business forecasting.

New algorithm reduces sample and communication complexities in federated Q-learning.