Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting. In this paper, we first make the connections between renewal processes, and a collection of current models used for intermittent demand forecasting. We then develop a set of models that benefit…
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48 results for “intermittent demand”
Unified framework for intermittent demand forecasting using renewal processes.
problem Intermittency in demand forecasting.
method Unified framework based on extensions of discrete-time renewal processes.
result Efficacy demonstrated in forecasting practice with favorable predictive accuracy.
A new metric optimizes forecasts for lumpy, intermittent demand.
problem Inaccurate demand forecasts lead to suboptimal logistics and production.
method Developed a novel metric that considers both statistical and business aspects.
result The new metric yields more accurate predictions for lumpy and intermittent demand.
A new Bayesian model improves forecasting for intermittent demand.
problem Sparse observations, cold-start items, and obsolescence in intermittent demand forecasting.
method Hierarchical Bayesian TSB model with partial pooling and calibrated probabilistic configuration.
result TSB-HB achieves the lowest RMSE and RMSSE on the UCI Online Retail dataset.
Proposes a pricing agent using reinforcement learning to balance renewable energy demand.
problem Intermittent renewable energy sources challenge carbon-free electricity generation.
method Reinforcement learning approach to balance customer demand with renewable energy generation.
result Demonstrates improved electricity pricing strategy for renewable energy integration.
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…
Global models outperform local models in forecasting intermittent time series.
problem Forecasting intermittent time series with zeros in supply chains.
method Comparison of state-of-the-art probabilistic local and global models on five datasets.
result TiDE, a simple neural network architecture, achieves the best accuracy among global models.
Dynamic pricing aims to match power supply and demand in an energy transition.
problem Mismatch between renewable energy supply and consumer demand.
method Formalizes decision-making problem, designs forecasting models, and statistical demand response models.
result Dynamic pricing can synchronise power supply and demand effectively.
Tab2vox converts tabular data into 3D images for improved demand forecasting.
problem Forecasting demand influenced by multi-level causes and large volatility.
method Tab2vox neural architecture search (NAS) model to convert tabular data into 3D voxel images for 3D CNN forecasting.
result 3D CNN forecasting model outperforms existing tabular data techniques.
New algorithm controls large groups of devices to match energy demand signals.
problem Controlling large populations of electrical devices to match energy demand signals.
method Developed MD-MFC algorithm for finite horizon Markovian mean field control problem.
result MD-MFC provides theoretical guarantees for convex and Lipschitz objective functions.
This paper proposes an agent-based model that combines both spot and balancing electricity markets. From this model, we develop a multi-agent simulation to study the integration of the consumers' flexibility into the system. Our study identifies the conditions that real-time prices may lead to higher electricity costs,…
This paper introduces intermittent learning - the goal of which is to enable energy harvested computing platforms capable of executing certain classes of machine learning tasks effectively and efficiently. We identify unique challenges to intermittent learning relating to the data and application semantics of machine l…
The study uses Gaussian Processes with Tweedie likelihood for forecasting intermittent time series.
problem Forecasting intermittent time series with high accuracy and flexibility.
method The approach combines Gaussian Processes with two forecast distributions: negative binomial and Tweedie.
result TweedieGP provides better probabilistic forecasts, especially for high quantiles.
SPECTRA improves probabilistic energy forecasting by separating trends and uncertainties.
problem Interacting uncertainties from renewable intermittency, demand flexibility, market volatility, and weather impact probabilistic forecasts.
method Adaptive state-space exogenous context and temporal-frequency resolution architecture.
result Achieved best CRPS in 14 out of 18 settings, reducing CRPS by 5.74% and upper-tail quantile risk by 7.27%.
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availabilitystat.ML
Federated learning algorithm improves with intermittent client availability.
problem Performance degradation in Federated Averaging due to client availability changes.
method Federated Latest Averaging (FedLaAvg) uses latest gradients from all clients, even when unavailable.
result FedLaAvg achieves sublinear speedup compared to classical Federated Averaging.
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…
Dynamical model of financial markets: fluctuating `temperature' causes intermittent behavior of price changescond-mat.stat-mech
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 model captures multifractal volatility in stock returns.
problem Capturing multifractal volatility in stock returns.
method Introduced mLog S-fBM model, defined mS-fBM, and developed calibration procedure.
result Model captures multifractal behavior in stock returns, validating on real data.
A new model captures multifractal volatility in stock returns.
problem Capturing multifractal volatility in stock returns.
method Introduced mLog S-fBM model, defined mS-fBM, and developed calibration procedure.
result Validated model on synthetic and real data, showing multifractal behavior.
Factorial moments are convenient tools in nuclear physics to characterize the multiplicity distributions when phase-space resolution () becomes small. For uncorrelated particle production within , Gaussian statistics holds and factorial moments are equal to unity for all orders . Correlations between par…
NeuralPrefix fills in missing sensor data without additional training.
problem Data intermittency in real-world sensing.
method NeuralPrefix is a task-agnostic, zero-shot imputation framework.
result NeuralPrefix accurately recovers missing samples and generalizes to unseen datasets.
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…
Sharp heat kernel estimates on manifolds lead to solutions of the Parabolic Anderson model.
problem Well-posedness and intermittency of solutions to the Parabolic Anderson model on Riemannian manifolds.
method Sharp global heat kernel bounds and geodesic comparison geometry.
result Upper and lower moment bounds for solutions of the Parabolic Anderson model on general compact Riemannian manifolds.
SurvSurf predicts first hitting times for intermittent events without monotonic violations.
problem Predicting first hitting times for intermittent events with monotonicity guarantees.
method Partially monotonic neural network for sequential events, incorporating unobserved events.
result SurvSurf outperforms existing models in MSE and IBS metrics.
Paper addresses state estimation in sensor networks with intermittent data.
problem State estimation in sensor networks with packet dropouts and corrupted observations.
method Bayesian variational inference with a dual-mask generative model.
result The method effectively identifies system states and noise parameters.
Forecasting Electricity Pricesq-fin.ST
Research focuses on predicting electricity prices with complex models considering probabilistic forecasts.
problem Challenging task due to market dynamics and weather and business activity dependence.
method Shift from econometric to statistical/machine learning models, considering probabilistic forecasts.
result More accurate predictions with complex models and probabilistic measures.
A new hierarchical forecasting method improves overall accuracy.
problem Hierarchical forecasting challenges, especially for intermittent time series.
method Top-down alignment of independent level forecasts using deep learning and tree-based algorithms.
result Improves overall forecasting accuracy compared to existing methods.
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…
Studying the impact of climate change on precipitation is constrained by finding a way to evaluate the evolution of precipitation variability over time. Classical approaches (feature-based) have shown their limitations for this issue due to the intermittent and irregular nature of precipitation. In this study, we prese…
Mini-batch sub-sampling in neural network training is unavoidable, due to growing data demands, memory-limited computational resources such as graphical processing units (GPUs), and the dynamics of on-line learning. In this study we specifically distinguish between static mini-batch sub-sampled loss functions, where mi…
ACI uses Bayesian data assimilation to trace causes from effects in complex systems.
problem Capturing instantaneous, time-evolving causal relationships in complex, high-dimensional systems.
method Assimilative causal inference (ACI) leverages Bayesian data assimilation to trace causes backward from observed effects.
result ACI provides online tracking of causal roles that may reverse intermittently and reveals how far effects propagate.
Intermittency and Localizationphysics.soc-ph
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, …
Deep learning improves weather modeling for electricity load forecasting.
problem Accurate load and renewable energy forecasting requires complex spatio-temporal weather modeling.
method Automated spatio-temporal feature extraction using deep neural networks.
result Deep learning outperforms traditional methods in French national load forecasting.
Model selection with proper scoring rules on data sets of time series: prefer the mean scaled scorestat.ML
Model selection for time series forecasting can be biased by the distribution of scores.
problem Model selection for probabilistic forecasting on time series data.
method Using proper scoring rules to aggregate scores across multiple time series.
result The mean score is immune to the skewness of the score distribution.
Paper shows local SGD outperforms mini-batch SGD under certain conditions.
problem Proving local SGD's superiority in distributed learning with heterogeneous data.
method New lower and upper bounds for local SGD under first-order heterogeneity assumptions.
result Local SGD is min-max optimal under certain conditions, resolving understanding of distributed optimization.
Non Poisson intermittent events in price formationphysics.data-an
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…
Data-driven ODE modeling of the high-frequency complex dynamics via a low-frequency dynamics modelnlin.CD
New method models complex dynamics using a base variable.
problem Modeling complex high-frequency dynamics from time series.
method Constructing a joint model with a base variable and a target variable.
result Successfully models chaotic behavior and reconstructs statistical properties.
Demand variance can result in a mismatch between planned supply and actual demand. Demand shaping strategies such as pricing can be used to shift elastic demand to reduce the imbalance. In this work, we propose to consider elastic demand in the forecasting phase. We present a method to reallocate the historical elastic…
The sharp peak-flat trough pattern and critical speculationcond-mat.stat-mech
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…
Study reveals dynamic causal relationships between Ethereum transaction fees and economic subsystems.
problem Historical gas fee volatility caused economic disequilibria and stakeholder challenges.
method Time-varying Granger causality analysis using data on active wallets and transaction volume.
result Dynamic bidirectional causal relationships between transaction fees and economic subsystems across Ethereum.
Modeling shared mobility demand considering supply limitations.
problem Inaccurate demand predictions due to limited supply.
method Censored Gaussian Processes for demand modeling.
result Taking supply limitations into account improves demand predictions.
The paper proposes a new model to better estimate demand from censored data.
problem Challenges in inferring true demand from aggregate, censored data.
method Combines Tobit likelihood with graph diffusion process in Gaussian Processes.
result The new model produces more accurate out-of-sample predictions.
Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecastingstat.AP
Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the general demand foreca…
Minimal Agent Based Model for Financial Markets I: Origin and Self-Organization of Stylized Factsq-fin.TR
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…
Paper optimizes FL communication efficiency with stochastic optimization.
problem Intermittent connectivity and non-i.i.d. data in FL.
method Convergence analysis of non-convex loss functions, stochastic optimization for client selection and power allocation.
result Significant reduction in communication time compared to random participation.
Study improves cross-modal bike-share and transit demand prediction.
problem Cross-modal ripple effects in urban transportation demand.
method Transfer learning and stacked LSTM models for cross-modal demand prediction.
result Transfer learning models outperform unimodal models in cross-modal demand prediction.