arXiv research

Proposes using elastic demand to improve forecasting accuracy.

Study optimizes smart contract adoption under high demand variability using Negative Binomial models.

Demand response is designed to motivate electricity customers to modify their loads at critical time periods. The accurate estimation of impact of demand response signals to customers' consumption is central to any successful program. In practice, learning these response is nontrivial because operators can only send a …

Study optimizes pricing under uncertainty and capacity constraints.

CD estimates demand by separating promotions from a stable base, reducing inventory costs.

The relationship between price volatilty and a market extremum is examined using a fundamental economics model of supply and demand. By examining randomness through a microeconomic setting, we obtain the implications of randomness in the supply and demand, rather than assuming that price has randomness on an empirical …

Accurate demand forecasts can help on-line retail organizations better plan their supply-chain processes. The challenge, however, is the large number of associative factors that result in large, non-stationary shifts in demand, which traditional time series and regression approaches fail to model. In this paper, we pro…

Study forecasts monthly electricity demand using pattern similarity-based methods.

In this article we quantify the bullwhip effect (the variance amplification in replenishment orders) when demands and lead times are predicted in a simple two-stage supply chain with one supplier and one retailer. In recent research the impact of stochastic order lead time on the bullwhip effect is investigated, but th…

Paper tackles learning win-win solutions in aggregation systems.

We present an online approach to portfolio selection. The motivation is within the context of algorithmic trading, which demands fast and recursive updates of portfolio allocations, as new data arrives. In particular, we look at two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS) and a regulari…

Model forecasts water demand with probabilistic multi-step-ahead approach.

Optimal insurance contract limits insurer's risk exposure variance.

MARS optimizes large model training by reducing variance, outperforming AdamW.

This research improves LSTM for monthly electricity demand forecasting using pattern-based methods.

Deep RL methods improve resource allocation in uncertain environments.

We study minority games in efficient regime. By incorporating the utility function and aggregating agents with similar strategies we develop an effective mesoscale notion of state of the game. Using this approach, the game can be represented as a Markov process with substantially reduced number of states with explicitl…

In this paper, we formulate a method for minimising the expectation value of the procurement cost of electricity in two popular spot markets: {\it day-ahead} and {\it intra-day}, under the assumption that expectation value of unit prices and the distributions of prediction errors for the electricity demand traded in tw…

The paper introduces a method to decompose variance in twin networks for better treatment effect estimation.

As application demands for zeroth-order (gradient-free) optimization accelerate, the need for variance reduced and faster converging approaches is also intensifying. This paper addresses these challenges by presenting: a) a comprehensive theoretical analysis of variance reduced zeroth-order (ZO) optimization, b) a nove…

Model predicts daily closing price distributions in call auctions.

Study of insurer games with model uncertainty in reinsurance and investment strategies.

Flexible model captures commodity skews with maturity effects.

Extends return extrapolation to nonlinear, asymmetric functions under stochastic volatility.

We extend return extrapolation to incorporate asymmetry and saturation, finding that asymmetric nonlinear extrapolation leads to lower welfare loss.

Collage inference uses redundancy to reduce cloud image classification latency variance.

New method reduces variance in Bayesian inverse problems.

The study examines insurance demand under rough volatility and path-dependent shocks.

A method for estimating nonlinear regression errors and their distributions without performing regression is presented. Assuming continuity of the modeling function the variance is given in terms of conditional probabilities extracted from the data. For N data points the computational demand is N2. Comparing the predic…

ISOMORPH creates a digital twin for supply chain logistics, advancing time-series forecasting benchmarks.

Develops a neural surrogate for proton dose calculation using Monte Carlo dropout uncertainty.

Optimizes reserve prices for first-price auctions to maximize revenue.

A new optimization method improves deep learning accuracy without hyper-parameter tuning.

Dynamic CBDT improves treatment effect estimation in clinical data.

New framework improves option pricing models by addressing volatility dynamics.

Study dynamic asset allocation in incomplete markets using game theory and nonlocal BSDEs.

Network models assume unrealistic idiosyncratic risk, which can be mitigated by allowing for correlated shocks.

Modeling shared mobility demand considering supply limitations.

Algorithms with fast convergence, small number of data access, and low per-iteration complexity are particularly favorable in the big data era, due to the demand for obtaining \emph{highly accurate solutions} to problems with \emph{a large number of samples} in \emph{ultra-high} dimensional space. Existing algorithms l…

The paper proposes a new model to better estimate demand from censored data.

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…

We introduce a simple agent-based model which allows us to analyze three stylized facts: a fat-tailed size distribution of companies, a `tent-shaped' growth rate distribution, the scaling relation of the growth rate variance with firm size, and the causality between them. This is achieved under the simple hypothesis th…

Study improves cross-modal bike-share and transit demand prediction.

State-space models win a forecasting competition for unstable data.

Develops price dynamics equations with symmetric supply/demand functions, affecting tail behavior of price distributions.

Independent component analysis (ICA) is a method for recovering statistically independent signals from observations of unknown linear combinations of the sources. Some of the most accurate ICA decomposition methods require searching for the inverse transformation which minimizes different approximations of the Mutual I…

A new metric optimizes forecasts for lumpy, intermittent demand.

Paper develops deep models for forecasting intermittent demand.