arXiv research

Introduces Spectral Attention for better long-range time series forecasting.

We consider the estimation of large covariance and precision matrices from high-dimensional sub-Gaussian or heavier-tailed observations with slowly decaying temporal dependence. The temporal dependence is allowed to be long-range so with longer memory than those considered in the current literature. We show that severa…

Multifractality in time series arises from temporal correlations, not just fat tails.

This paper looks into the analysis of the long-range auto-correlations and cross-correlations in bond market. Based on Detrended Moving Average (DMA) method, empirical results present a clear evidence of long-range persistence that exists in one year scale. The degree of long-range correlation related to maturities has…

We investigate how simultaneously recorded long-range power-law correlated multi-variate signals cross-correlate. To this end we introduce a two-component ARFIMA stochastic process and a two-component FIARCH process to generate coupled fractal signals with long-range power-law correlations which are at the same time lo…

Transformer-based method for causal discovery with prior knowledge integration.

TK-GCN forecasts spatiotemporal dynamics using Koopman-enhanced graph convolutional networks.

Wave-U-Net improves audio source separation by modeling phase information.

Digital currencies exhibit multifractality due to heavy-tailed returns and temporal correlations.

We introduce a new method for detection of long-range cross-correlations and multifractality - multifractal height cross-correlation analysis (MF-HXA) - based on scaling of qth order covariances. MF-HXA is a bivariate generalization of the height-height correlation analysis of Barabasi & Vicsek [Barabasi, A.L., Vicsek,…

This paper studies mutual information in sequence models and finds Transformers excel in capturing long-range dependencies.

FPG uses fractional calculus for efficient reinforcement learning with long-term memory.

We introduce a new test for detection of power-law cross-correlations among a pair of time series - the rescaled covariance test. The test is based on a power-law divergence of the covariance of the partial sums of the long-range cross-correlated processes. Utilizing a heteroskedasticity and auto-correlation robust est…

Deep learning model predicts traffic flows across entire network for multiple steps ahead.

New method predicts spatio-temporal data with short and long-range dependence.

Long-range correlation in financial time series reflects the complex dynamics of the stock markets driven by algorithms and human decisions. Our analysis exploits ultra-high frequency order book data from NASDAQ Nordic over a period of three years to numerically estimate the power-law scaling exponents using detrended …

Estimates LRD in sequential data, improving RNNs.

We apply a recently developed wavelet based approach to characterize the correlation and scaling properties of non-stationary financial time series. This approach is local in nature and it makes use of wavelets from the Daubechies family for detrending purpose. The built-in variable windows in wavelet transform makes t…

Paper uses DMD to embed time in spatiotemporal forecasting.

Econophysics explores power-law correlations in financial markets.

This work predicts and interpolates long-range videos using unsupervised landmarks.

Previous studies indicate that nonlinear properties of Gaussian time series with long-range correlations, $u_i$, can be detected and quantified by studying the correlations in the magnitude series $|u_i|$, i.e., the ``volatility''. However, the origin for this empirical observation still remains unclear, and the exact …

TFiLM expands convolutional models' receptive field with minimal overhead.

This study uses moving average cluster entropy to analyze financial market dynamics.

Neural M3 model adapts to diverse user behaviors over short and long timeframes.

Paper proposes an active learning method for surgical workflow recognition using long-range temporal dependency.

A method for estimating the cross-correlation $C_{xy}(τ)$ of long-range correlated series $x(t)$ and $y(t)$, at varying lags $τ$ and scales $n$, is proposed. For fractional Brownian motions with Hurst exponents $H_1$ and $H_2$, the asymptotic expression of $C_{xy}(τ)$ depends only on the lag $τ$ (wide-sense stationarit…

Develops a bi-variate stochastic framework to model mortality and interest rates with long-range dependence.

With the daily and minutely data of the German DAX and Chinese indices, we investigate how the return-volatility correlation originates in financial dynamics. Based on a retarded volatility model, we may eliminate or generate the return-volatility correlation of the time series, while other characteristics, such as the…

Forecaster uses graph Transformers to forecast spatial and time-dependent data.

Within the framework of maximum entropy principle we show that the finite-size long-range Ising model is the adequate model for the description of homogeneous credit portfolios and the computation of credit risk when default correlations between the borrowers are included. The exact analysis of the model suggest that w…

The distribution of recurrence times or return intervals between extreme events is important to characterize and understand the behavior of physical systems and phenomena in many disciplines. It is well known that many physical processes in nature and society display long range correlations. Hence, in the last few year…

MarketGAN generates financial returns using GANs to match empirical stylized facts.

A new method uses burst and inter-burst duration to test long-range memory in financial markets.

We propose a general interpretation for long-range correlation effects in the activity and volatility of financial markets. This interpretation is based on the fact that the choice between `active' and `inactive' strategies is subordinated to random-walk like processes. We numerically demonstrate our scenario in the fr…

Mutually interacting components form complex systems and the outputs of these components are usually long-range cross-correlated. Using wavelet leaders, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call joint multifractal analysis based on wavel…

Many complex systems generate multifractal time series which are long-range cross-correlated. Numerous methods have been proposed to characterize the multifractal nature of these long-range cross correlations. However, several important issues about these methods are not well understood and most methods consider only o…

Graph WaveNet models spatial-temporal graphs by learning hidden dependencies and long sequences.

Complex systems are composed of mutually interacting components and the output values of these components are usually long-range cross-correlated. We propose a method to characterize the joint multifractal nature of such long-range cross correlations based on wavelet analysis, termed multifractal cross wavelet analysis…

Inspired by the recent literature on aggregation theory, we aim at relating the long range correlation of the stocks return volatility to the heterogeneity of the investors' expectations about the level of the future volatility. Based on a semi-parametric model of investors' anticipations, we make the connection betwee…

New method interprets quantum many-body snapshots for phase detection.

Generative models learn from high-level music representations, but this work models music in raw audio.

Study finds long-range dependence in financial markets, but deep generative models struggle to replicate it.

We review ideas on temporal dependences and recurrences in discrete time series from several areas of natural and social sciences. We revisit existing studies and redefine the relevant observables in the language of copulas (joint laws of the ranks). We propose that copulas provide an appropriate mathematical framework…

Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering arbitrarily long pasts to retain information about arbitrarily long futures requires resources that typically grow exponentially with length. The challenge is compounded for infinite-order Markov processes, since conditioning on fi…

TCGPN improves stock forecasting by capturing temporal correlation patterns.

We show how to analyze and interpret the correlation structures, the conditional expectation values and correlation coefficients of exchangeable Bernoulli random variables. We study implied default distributions for the iTraxx-CJ tranches and some popular probabilistic models, including the Gaussian copula model, Beta …

MTRGL learns temporal correlations from multi-modal data for improved pair trading.