arXiv research

Proposes a new test for validating multivariate dynamic regression models.

Research forecasts electricity spot prices using stochastic volatility models.

A method for identifying NPWARX models with arbitrary domains using probabilistic mixture models.

We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is tackled using sparse Gaussian processes (GP). We integrate data pre-processing with system identification into a fully …

In this paper we investigate the impact of news to predict extreme financial returns using high frequency data. We consider several model specifications differing for the dynamic property of the underlying stochastic process as well as for the innovation process. Since news are essentially qualitative measures, they ar…

Study uses TDA to improve OEE forecasting in manufacturing.

GaussDetect-LiNGAM eliminates Gaussianity tests for causal discovery.

The paper models ATM cash withdrawal chaos and forecasts using deep learning.

We present the first tree-based regressor whose convergence rate depends only on the intrinsic dimension of the data, namely its Assouad dimension. The regressor uses the RPtree partitioning procedure, a simple randomized variant of k-d trees.

The paper tackles reinforcement learning with exogenous variables and rewards.

Dynamic regressor selection (DRS) systems work by selecting the most competent regressors from an ensemble to estimate the target value of a given test pattern. This competence is usually quantified using the performance of the regressors in local regions of the feature space around the test pattern. However, choosing …

SPACR trains uncertainty-aware regressors directly within a single pass, improving efficiency and validity.

Exogenous state variables and rewards can slow down reinforcement learning by injecting uncontrolled variation into the reward signal. We formalize exogenous state variables and rewards and identify conditions under which an MDP with exogenous state can be decomposed into an exogenous Markov Reward Process involving on…

We introduce a new learned descriptor for audio signals which is efficient for event representation. The entries of the descriptor are produced by evaluating a set of regressors on the input signal. The regressors are class-specific and trained using the random regression forests framework. Given an input signal, each …

Estimates hybrid dynamical systems with polynomial expansions and Markovian switching.

High-precision machine learning reduces particle physics simulations by orders of magnitude.

We address microscopic, agent based, and macroscopic, stochastic, modeling of the financial markets combining it with the exogenous noise. The interplay between the endogenous dynamics of agents and the exogenous noise is the primary mechanism responsible for the observed long-range dependence and statistical propertie…

Study shows $k$-NN regressor consistency in complex survey designs.

New TSER algorithms outperform existing methods in time series extrinsic regression.

A large number and diversity of techniques have been offered in the literature in recent years for solving multi-label classification tasks, including classifier chains where predictions are cascaded to other models as additional features. The idea of extending this chaining methodology to multi-output regression has a…

New ARIMA framework improves forecast accuracy for economic and financial time series.

The study evaluates nine machine learning regressors for predicting NASDAQ stock opening prices.

We focus in this paper on high-dimensional regression problems where each regressor can be associated to a location in a physical space, or more generally a generic geometric space. Such problems often employ sparse priors, which promote models using a small subset of regressors. To increase statistical power, the so-c…

Study classifies stock price jumps as exogenous or endogenous using news data.

New method learns exogenous variable distributions for better causal optimization.

Study analyzes climate impact on agricultural prices, offering insurance solutions.

Paper introduces $\ell$-DER for regression tasks using morphological operators and convex-concave procedure.

Autonomy and adaptation of machines requires that they be able to measure their own errors. We consider the advantages and limitations of such an approach when a machine has to measure the error in a regression task. How can a machine measure the error of regression sub-components when it does not have the ground truth…

We study Exo-MDPs to reduce sample complexity in reinforcement learning.

This paper uses NARX neural networks for macroeconomic forecasting and goal setting.

Time series prediction has been studied in a variety of domains. However, it is still challenging to predict future series given historical observations and past exogenous data. Existing methods either fail to consider the interactions among different components of exogenous variables which may affect the prediction ac…

New approach uses 'forward-looking' counterfactuals for treatment choice.

The paper analyzes binary option markets with exogenous information and price sensitivity.

Enhances random forest performance with exogenous randomness.

HL algorithms improve resource allocation in cloud environments.

Systems with long-range persistence and memory are shown to exhibit different precursory as well as recovery patterns in response to shocks of exogeneous versus endogeneous origins. By endogeneous, we envision either fluctuations resulting from an underlying chaotic dynamics or from a stochastic forcing origin which ma…

Improved electricity price forecasting with NBEATSx model.

Method estimates exogenous and endogenous factors from event times.

We consider the problem of identifying the causal direction between two discrete random variables using observational data. Unlike previous work, we keep the most general functional model but make an assumption on the unobserved exogenous variable: Inspired by Occam's razor, we assume that the exogenous variable is sim…

Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta-learning framework, ensemble techniques can easily be applied to many machine learning methods. Inspired by ensemble techniques, in this paper we propose an ensemble loss functions applied to a simple regre…

Study liquidity provision with exogenous competition using a reference market maker.

This work tackles fitting Hawkes processes to interval-censored data.

We propose a new algorithm for estimating NARMAX models with $L_1$ regularization for models represented as a linear combination of basis functions. Due to the $L_1$-norm penalty the Lasso estimation tends to produce some coefficients that are exactly zero and hence gives interpretable models. The novelty of the contri…

Study improves flood loss risk models using historical data and rainfall data.

Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations (p<n, p: the number of variables and n: the number of observations). However, modern datasets including gene expression data need high-dimensional causal modeling in challenging situation…

Paper simplifies complex causal identifiability problems with exogenous isomorphism.

Boosting ridge regression for high-dimensional data classification reduces computational cost and improves learning time.

We reverse engineer dynamics of financial contagion to find the scenario of smallest exogenous shock that, should it occur, would lead to a given final systemic loss. This reverse stress test can be used to identify the potential triggers of systemic events, and it removes the arbitrariness in the selection of shock sc…