arXiv research

Paper introduces a new method for classifying interval-valued time series.

New method forecasts values and timing in irregular time series.

Study shows financial value of weak information converges in discrete vs continuous markets.

RDIS fills missing values in time series data explicitly.

Proposes adaptive method for classifying interval-valued time series.

Study on 2-valued dynamics on complex plane, showing some dynamics can't be group actions.

This research improves value-at-risk estimation during financial crises using non-extensive statistical methods.

This work extends set-valued risk measures to discrete time, using difference inclusions and equations.

Revisits superhedging under proportional costs in continuous time markets.

The time value of money is a critical factor not only in risk analysis, but also in insurance and financial applications. In this paper, we consider a special class of set-valued risk statistics by introducing the time value of money. In fact, the risk statistics established by this method is closer to financial realit…

Generally accepted depreciation methods do not compute the intrinsic value of an asset, as they do not factor for the Time Value of Money, a key principle within financial theory. This is disadvantageous, as knowing the intrinsic value of an asset can assist with making effective purchase and sale decisions. By applyin…

Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the …

New framework predicts time series with missing values without imputation.

CoIFNet unifies imputation and forecasting for robust multivariate time series prediction with missing values.

A method for calculating multi-portfolio time consistent multivariate risk measures in discrete time is presented. Market models for $d$ assets with transaction costs or illiquidity and possible trading constraints are considered on a finite probability space. The set of capital requirements at each time and state is c…

This research adapts superpixels for Shapley value computation in DNA profile classification.

Bayesian analysis of financial time series using R-INLA.

We study an optimal execution problem with uncertain market impact to derive a more realistic market model. We construct a discrete-time model as a value function for optimal execution. Market impact is formulated as the product of a deterministic part increasing with execution volume and a positive stochastic noise pa…

We consider that the price of a firm follows a non linear stochastic delay differential equation. We also assume that any claim value whose value depends on firm value and time follows a non linear stochastic delay differential equation. Using self-financed strategy and replication we are able to derive a Random Partia…

The paper concerns primal and dual representations as well as time consistency of set-valued dynamic risk measures. Set-valued risk measures appear naturally when markets with transaction costs are considered and capital requirements can be made in a basket of currencies or assets. Time consistency of scalar risk measu…

Time-variant value function transfer method for RL.

Equivalent characterizations of multiportfolio time consistency are deduced for closed convex and coherent set-valued risk measures on $L^p(Ω,\mathcal F, P; R^d)$ with image space in the power set of $L^p(Ω,\mathcal F_t,P;R^d)$. In the convex case, multiportfolio time consistency is equivalent to a cocycle condition on…

In this paper we discuss a credit risk model with a pure jump Lévy process for the asset value and an unobservable random barrier. The default time is the first time when the asset value falls below the barrier. Using the indistinguishability of the intensity process and the likelihood process, we prove the existence o…

A new model calculates LGD distribution based on firm value and credit market conditions.

Paper uses RNN to predict SaaS user lifetime value.

Solves the equity premium puzzle without calibrated values.

Time series prediction with missing values is an important problem of time series analysis since complete data is usually hard to obtain in many real-world applications. To model the generation of time series, autoregressive (AR) model is a basic and widely used one, which assumes that each observation in the time seri…

ProFITi model forecasts irregular time series with missing values using conditional flows.

Suppose you have one unit of stock, currently worth 1, which you must sell before time $T$. The Optional Sampling Theorem tells us that whatever stopping time we choose to sell, the expected discounted value we get when we sell will be 1. Suppose however that we are able to see $a$ units of time into the future, and ba…

Framework for joint learning of tasks on dementia data with missing values.

It will be discussed the statistics of the extreme values in time series characterized by finite-term correlations with non-exponential decay. Precisely, it will be considered the results of numerical analyses concerning the return intervals of extreme values of the fluctuations of resistance and defect-fraction displa…

This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumpt…

Efficiently predicts long-time dynamics of quantum spin models using MLP regression.

Framework for imputing time series data with uncertainty measures.

Investigates set-valued risk measures for processes and vectors, proving equivalence and providing new dual representations.

TD learning reduces prediction error in Markov chain problems.

LSSDM improves imputation of multivariate time series data.

We consider a basic problem at the interface of two fundamental fields: submodular optimization and online learning. In the online unconstrained submodular maximization (online USM) problem, there is a universe $[n]=\{1,2,...,n\}$ and a sequence of $T$ nonnegative (not necessarily monotone) submodular functions arrive …

Paper introduces SMM for forecasting multiple time series with missing values.

The paper examines extreme value statistics of high-dimensional sample covariances, with applications in finance and image analysis.

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a.…

Ensemble method detects time series anomalies without preselecting parameter values.

There is a need for the development of models that are able to account for discreteness in data, along with its time series properties and correlation. Our focus falls on INteger-valued AutoRegressive (INAR) type models. The INAR type models can be used in conjunction with existing model-based clustering techniques to …

Improved time series classification with imputed data using label-guided forest-based methods.

Invariant complex structures on the homogeneous manifold $U(n+1)/U(n)\times U(p+1)/U(p)$ are reseached. Extreme values of sectional curvature of Hermitian metrics on this manifold are found.

In this paper we study time-consistent risk measures for returns that are given by a GARCH(1,1) model. We present a construction of risk measures based on their static counterparts that overcomes the lack of time-consistency. We then study in detail our construction for the risk measures Value-at-Risk (VaR) and Average…

Study finds weak solutions for complex map flows with optimal lifespan.

Q($Δ$)-Learning improves Q-Learning by separating action-value functions into different time scales.