arXiv research

ERNN improves RNN accuracy and stability with time-delayed self-feedback.

TSMB handles time delays in multivariate time series data.

BayTiDe discovers time-delayed differential equations from noisy data.

Study on synchronization in financial markets with time delays.

In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here we composed an empirical wavelet from the SETAR (Self-Exciting Threshold Autoregressive) model and identified the thres…

New method estimates traffic congestion delays using statistical causality.

In this paper we investigate novel applications of a new class of equations which we call time-delayed backward stochastic differential equations. Time-delayed BSDEs may arise in finance when we want to find an investment strategy and an investment portfolio which should replicate a liability or meet a target depending…

This paper provides a mathematical framework for time-delay reservoir computing.

In this paper we show that several dynamical systems with time delay can be described as vector fields associated to smooth functions via a bracket of Leibniz structure. Some examples illustrate the theoretical considerations.

Time-delayed embeddings avoid self-intersections for high enough delay.

Improved GRU model with weighted time-delay feedback for long-term dependencies.

Predicting conversion rates (CVRs) in display advertising (e.g., predicting the proportion of users who purchase an item (i.e., a conversion) after its corresponding ad is clicked) is important when measuring the effects of ads shown to users and to understanding the interests of the users. There is generally a time de…

In this paper we consider backward stochastic differential equations with time-delayed generators of a moving average type. The classical framework with linear generators depending on $(Y(t),Z(t))$ is extended and we investigate linear generators depending on $(\frac{1}{t}\int_0^tY(s)ds, \frac{1}{t}\int_0^tZ(s)ds)$. We…

Improved modeling of chaotic systems using time-delay embeddings and Frenet-Serret frame.

This paper presents a stochastic logic time delay reservoir design. The reservoir is analyzed using a number of metrics, such as kernel quality, generalization rank, performance on simple benchmarks, and is also compared to a deterministic design. A novel re-seeding method is introduced to reduce the adverse effects of…

Measures time-delay embedding for noisy, sparse data.

Develops variable-lag Granger causality for more accurate time series analysis.

Survey of RL methods for control systems with time delays.

Develops variable-lag Granger causality and Transfer Entropy for time series analysis.

New method disentangles latent variables in nonstationary data.

ERDMD discovers sparse, nonuniformly timed DMD models from chaotic attractors.

We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of interm…

TreeDOX predicts chaotic systems without hyperparameter tuning.

The paper analyzes momentum in deep learning from a regularization viewpoint.

Neural Laplace Control tackles offline RL for continuous-time delayed systems with irregular observations.

This paper optimizes autonomous vehicle controllers using data-driven methods.

A fast method for approximate TDE in ultrasound elastography.

There is a large amount of interest in understanding users of social media in order to predict their behavior in this space. Despite this interest, user predictability in social media is not well-understood. To examine this question, we consider a network of fifteen thousand users on Twitter over a seven week period. W…

Algorithm improves RL by discovering delayed causal relations.

In this paper we present a continuous time dynamical model of heterogeneous agents interacting in a financial market where transactions are cleared by a market maker. The market is composed of fundamentalist, trend following and contrarian agents who process information from the market with different time delays. Each …

We review the spectral analysis and the time-dependent approach of scattering theory for manifolds with asymptotically cylindrical ends. For the spectral analysis, higher order resolvent estimates are obtained via Mourre theory for both short-range and long-range behaviors of the metric and the perturbation at infinity…

We consider the static and dynamic models of Cournot duopoly with tax evasion. In the dynamic model we introduce the time delay and we analyze the local stability of the stationary state. There is a critical value of the delay when the Hopf bifurcation occurs.

Framework LiLY recovers latent causal variables from time-series data under distribution shifts.

Backpropagation is explained as a diffusion process in neural networks.

Parsimonious Dynamic Mode Decomposition selects sparse modes robustly.

The study finds a time lag effect in FDI-GDP correlations, with significant statistical significance.

Training deep recurrent neural network (RNN) architectures is complicated due to the increased network complexity. This disrupts the learning of higher order abstracts using deep RNN. In case of feed-forward networks training deep structures is simple and faster while learning long-term temporal information is not poss…

New method constructs multilayer networks from financial data, capturing dependencies across different risk factors.

A novel observer-based method detects and recovers anomalies in CAV sensor readings.

This study uses persistent homology to analyze complex transitional networks from time series data.

New framework TDRL identifies latent causal variables from sequential data.

Optimizes asset allocation with illiquid assets using MPC.

New method recovers radar and communication signals from overlaid data.

Black-box attacks on RL agents using temporal information.

Solves utility maximization for delayed informed investors.

Many studies have shown that there are good reasons to claim very low predictability of currency nevertheless, the deviations from true randomness exist which have potential predictive and prognostic power [J.James, Quantitative finance 3 (2003) C75-C77]. We analyze the local trends which are of the main focus of the t…

We show that time-dependent fluctuations $\{Δx\}$ in foreign exchange rates are accurately described by a random walk in a complex plane that is demarcated into the gain (+) and loss (-) sectors. $\{Δx\}$ is the outcome of $N$ random steps from the origin and $|Δx|$ is the square of the Euclidean distance of the final …

The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested …