arXiv research

This paper reviews deep time-series forecasting focusing on autocorrelation modeling.

This paper examines autocorrelation in major crypto markets, finding persistent correlations on short time frames.

Proposes adjusting neural network errors for time series forecasting.

FreDF improves forecasting by learning in the frequency domain.

The paper examines how market trade values and volumes affect price autocorrelation.

Novel method discovers causal relations in time series data, even with autocorrelation.

Framework isolates causal effects from time series data, improving accuracy under non-stationarity and autocorrelation.

Estimates price elasticity from autocorrelated time series using causal graphs.

Study of autocorrelation times in neural MCMC simulations for the 2D Ising model.

New method improves causal discovery in time series with latent confounders.

A new RL framework handles autocorrelated actions for better learning and stability.

We present an original and novel method based on random matrix approach that enables to distinguish the respective role of temporal autocorrelations inside given time series and cross correlations between various time series. The proposed algorithm is based on properties of Wigner eigenspectrum of random matrices inste…

We describe the impact of the intra-day activity pattern on the autocorrelation function estimator. We obtain an exact formula relating estimators of the autocorrelation functions of non-stationary process to its stationary counterpart. Hence, we proved that the day seasonality of inter-transaction times extends the me…

Improved deep probabilistic time series forecasting by learning error autocorrelation.

We develop a framework especially suited to the autocorrelation properties observed in financial times series, by borrowing from the physical picture of turbulence. The success of our approach as applied to high frequency foreign exchange data is demonstrated by the overlap of the curves in Figure (1), since we are abl…

Introduces a new Hawkes model with CARMA(p,q) intensity to better model dependence structures.

Study GLS estimator properties in multivariate regression with heteroskedastic and autocorrelated errors.

We study the activity, i.e., the number of transactions per unit time, of financial markets. Using the diffusion entropy technique we show that the autocorrelation of the activity is caused by the presence of peaks whose time distances are distributed following an asymptotic power law which ultimately recovers the Pois…

Financial market dynamics is rigorously studied via the exact generalized Langevin equation. Assuming market Brownian self-similarity, the market return rate memory and autocorrelation functions are derived, which exhibit an oscillatory-decaying behavior with a long-time tail, similar to empirical observations. Individ…

As deep Variational Auto-Encoder (VAE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the VAE architecture to concurrently maximize the timescale of the latent space while inferring a reduced coordinate, which assists in finding slow processes as according t…

This paper speeds up Gaussian process regression for autocorrelated data.

HopCPT improves conformal prediction for time series with temporal dependencies.

The paper uncovers the impact of price and payoff autocorrelations in multi-period asset pricing models.

AUCRSS detects change points in partially observed multivariate autocorrelated data.

New framework uses time series features for predicting streamflow in ungauged areas.

Proposes QDF to improve multi-step time-series forecasting.

New adaptive temperature selection improves parallel tempering efficiency.

Sketching reduces data size for accurate spectral estimation.

Analyzed Bitcoin market index volatility changes over two distinct periods using anomalous diffusion and multifractal analysis.

We exploit a continuous time random walk description of stock prices to obtain a fast and accurate evaluation of their volatility from intraday data. We show that financial markets are usefully described as open physical systems. Indeed we find that the process determining market volatility is not stationary while the …

The mean-field variant of the model of limit order driven market introduced recently by Maslov is formulated and solved. The agents do not have any strategies and the memory of the system is kept within the order book. We show that he evolution of the order book is governed by a matrix multiplicative process. The resul…

Generative adversarial networks with attention improve financial time series simulation.

It is common to subsample Markov chain output to reduce the storage burden. Geyer (1992) shows that discarding $k-1$ out of every $k$ observations will not improve statistical efficiency, as quantified through variance in a given computational budget. That observation is often taken to mean that thinning MCMC output ca…

The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it…

Leveraged ETFs can outperform their targets in certain market conditions, contrary to the volatility drag hypothesis.

The paper calculates optimal trading turnover in terms of asset liquidity and alpha autocorrelation.

Causal relationships in time series with latent variables are discovered using LPCMCI.

We propose a new Directed Continuous-Time Random Walk (CTRW) model with memory. As CTRW trajectory consists of spatial jumps preceded by waiting times, in Directed CTRW, we consider the case with only positive spatial jumps. Moreover, we consider the memory in the model as each spatial jump depends on the previous one.…

We perform a scaling analysis on NYSE daily returns. We show that volatility correlations are power-laws on a time range from one day to one year and, more important, that they exhibit a multiscale behaviour.

Algorithm constructs coresets for clustering time series data from Gaussian mixtures.

In many physical, social or economical phenomena we observe changes of a studied quantity only in discrete, irregularly distributed points in time. The stochastic process used by physicists to describe this kind of variables is the Continuous Time Random Walk (CTRW). Despite the popularity of this type of stochastic pr…

Growth-optimal portfolios are guaranteed to accumulate higher wealth than any other investment strategy in the long run. However, they tend to be risky in the short term. For serially uncorrelated markets, similar portfolios with more robust guarantees have been recently proposed. This paper extends these robust portfo…

We propose an artificial market model based on deterministic agents. The agents modify their ask/bid price depending on past price changes. The temporal development of market price fluctuations is calculated numerically. A probability density function of market price changes has power law tails. Autocorrelation coeffic…

This study analyses, through cross-section estimation methods, the influence of spatial effects in productivity (product per worker), at economic sectors level of the NUTs III of mainland Portugal, from 1995 to 1999 and from 2000 to 2005 (taking in count the data availability and the Portuguese and European context), c…

Improved online changepoint detection for autocorrelated data.

It is well known that there exist statistical and structural differences between the stock markets of developed and emerging countries. In this work, we present an analysis of the variations and autocorrelations of the Mexican Stock Market index (IPC) for different periods of its historical daily data, showing evidence…

The study examines cryptocurrency market activity, revealing multifractal inter-transaction times and challenging traditional statistical models.

Continuous-time model shows insider trading constraints impact market dynamics.