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…
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48 results for “spatial autocorrelations”
The consideration of spatial effects at a regional level is becoming increasingly frequent and the work of Anselin (1988), among others, has contributed to this. This study analyses, through cross-section estimation methods, the influence of spatial effects in productivity (product per worker) in the NUTs III economic …
This study analyses, through cross-section estimation methods, the influence of spatial effects in the conditional product convergence in the parishes' economies of mainland Portugal between 1991 and 2001 (the last year with data available for this spatial disaggregation level). To analyse the data, Moran's I statistic…
This study analyses, through cross-section estimation methods, the influence of spatial effects and human capital in the conditional productivity convergence (product per worker) in the economic sectors of NUTs III of mainland Portugal between 1995 and 2002. To analyse the data, Moran's I statistics is considered, and …
Machine learning models perform better with location coordinates alone, not Moran Eigenvectors.
problem Improving machine learning models for spatial data.
method Examined Moran Eigenvectors as additional spatial features in machine learning models using synthetic datasets.
result Machine learning models using only location coordinates achieve better accuracies than eigenvector-based approaches.
Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is problematic and results in models that can reproduce training data …
SXL embeds spatial autocorrelation into neural networks for better geographic data learning.
problem Difficulties in learning spatial effects for neural networks in geographic data.
method SXL uses auxiliary tasks and autoregressive embeddings to learn spatial autocorrelation.
result SXL improves neural network training in unsupervised and supervised learning tasks.
Detecting spatial homogeneity in the world trade web with Detrended Fluctuation Analysisphysics.data-an
In a spatially embedded network, that is a network where nodes can be uniquely determined in a system of coordinates, links' weights might be affected by metric distances coupling every pair of nodes (dyads). In order to assess to what extent metric distances affect relationships (link's weights) in a spatially embedde…
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…
New method generates geolocated synthetic populations from real data.
problem Generating synthetic populations with explicit geographic coordinates.
method Mapping coordinates into a latent space using Normalizing Flows (NF), then combining with other features in a Variational Autoencoder (VAE).
result NF+VAE architecture outperforms existing methods in generating geolocated synthetic populations.
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.…
Proposes a method to make statistical inferences robust in spatially dependent settings with missing at random labels.
problem Statistical inference challenges with missing at random labels and spatial dependence.
method Doubly robust estimator with cross-fit nuisances and jackknife spatial HAC variance correction.
result Asymptotically valid confidence intervals with improved finite-sample calibration.
State-of-the-art deep learning methods have shown a remarkable capacity to model complex data domains, but struggle with geospatial data. In this paper, we introduce SpaceGAN, a novel generative model for geospatial domains that learns neighbourhood structures through spatial conditioning. We propose to enhance spatial…
CAST package aids in spatial prediction models using machine learning.
problem Challenges in applying machine learning for spatial data.
method Developed cross-validation strategies, spatial feature selection, and area of applicability assessment methods.
result Supports more reliable spatial predictions through CAST package.
Paper examines global Covid-19 data complexity and finds low intrinsic dimensions.
problem Understanding the complexity of Covid-19 data across countries.
method Used a Bayesian mixture model (Hidalgo) to estimate intrinsic dimensionality.
result Covid-19 data projects onto two low-dimensional manifolds without significant loss of information.
Hybrid model integrates GATv2 and geostatistics for better spatial prediction and uncertainty.
problem Accurate spatial prediction and uncertainty quantification in epidemiology and risk analysis.
method Integrates Graph Attention Network (GATv2) with model-based geostatistics (MBG) to capture relational and spatial dependencies.
result Hybrid model improves predictive accuracy and uncertainty quantification compared to standalone models.
As one of the important functions of the intelligent transportation system (ITS), supply-demand prediction for autonomous vehicles provides a decision basis for its control. In this paper, we present two prediction models (i.e. ARLP model and Advanced ARLP model) based on two system environments that only the current d…
Study GLS estimator properties in multivariate regression with heteroskedastic and autocorrelated errors.
problem Asymptotic properties of GLS estimator in multivariate regression with specific error structures.
method Derive Wald statistics for linear restrictions and assess their performance.
result Wald statistics remain robust to heteroskedasticity and autocorrelation.
Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in co…
The paper examines how market trade values and volumes affect price autocorrelation.
problem Understanding the impact of market trade values and volumes on price autocorrelation.
method Derives the dependence of price statistical moments and volatility on trade values and volumes, and assesses statistical moments and correlations by conventional frequency-based probabilities.
result Highlights the impact of market trade randomness on price statistical moments and autocorrelation.
This paper speeds up Gaussian process regression for autocorrelated data.
problem Temporal overfitting in Gaussian process models for autocorrelated data.
method Modifying existing Gaussian process approximations to handle blocked, de-correlated data.
result Proposed methods accelerate Gaussian process regression on autocorrelated data without sacrificing performance.
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lassostat.ML
New method controls false detections in brain activity localization.
problem Statistical control of false detections in brain activity localization.
method Adapted Lasso estimator for spatio-temporal MEG/EEG data.
result Offers statistical guarantees and adaptive method for thresholding.
This paper reviews deep time-series forecasting focusing on autocorrelation modeling.
problem Modeling autocorrelation in history and label sequences for time-series forecasting.
method Proposes a novel taxonomy for model architectures and learning objectives.
result Provides a comprehensive review and analysis of deep time-series forecasting.
The paper uncovers the impact of price and payoff autocorrelations in multi-period asset pricing models.
problem Hidden dependence of asset pricing models on price and payoff autocorrelations.
method Obtained approximations of the basic pricing equation describing various parameters.
result Valid results for other pricing models like ICAPM and APM.
FreDF improves forecasting by learning in the frequency domain.
problem Label autocorrelation in future forecasts is often overlooked in time series modeling.
method FreDF learns to forecast in the frequency domain to mitigate label autocorrelation.
result FreDF significantly outperforms existing methods in forecasting accuracy.
New framework for disentangling features from noisy data.
problem Disentangling identifiable features from noisy data.
method Structured Nonlinear Independent Component Analysis (SNICA).
result Identifiability holds even in the presence of noise of unknown distribution.
This paper examines autocorrelation in major crypto markets, finding persistent correlations on short time frames.
problem Assessing the efficiency of major cryptocurrency markets through autocorrelation analysis.
method Pearson's autocorrelation coefficient, Ljung-Box test, rolling window analysis.
result Persistent autocorrelation on 5m and 1H time frames, disagreement on 1D and 1W time frames.
Proposes adjusting neural network errors for time series forecasting.
problem Autocorrelated errors in neural networks for time series.
method Jointly learn autocorrelation coefficient with model parameters.
result Enhances performance in almost all time series forecasting cases.
Package {mlr3spatiotempcv} simplifies spatiotemporal resampling methods in R.
problem Assessing and tuning spatial and spatiotemporal machine learning models.
method Integrates various spatiotemporal resampling methods into the {mlr3} framework.
result Provides a consistent interface for spatiotemporal resampling methods.
Novel method discovers causal relations in time series data, even with autocorrelation.
problem Discovering causal relations in time series data with strong autocorrelation.
method Conditional independence (CI) based PCMCI method, optimized for contemporaneous and lagged links.
result PCMCI outperforms other methods in detecting causal links and controlling false positives.
The paper calculates optimal trading turnover in terms of asset liquidity and alpha autocorrelation.
problem Understanding optimal trading turnover in the context of asset liquidity and alpha autocorrelation.
method Developed a Gaussian process model to compute steady-state turnover explicitly, relating it to asset liquidity and alpha autocorrelation.
result Steady-state optimal turnover is given by , where is a liquidity-adjusted risk-aversion and is the mean-reversion speed ratio.
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…
Estimates price elasticity from autocorrelated time series using causal graphs.
problem Inconsistent IV estimators in autocorrelated time series data.
method Model equilibrium with unobserved confounders, derive DAG, and use graphical inference for valid IV estimators.
result Valid IV estimators improve understanding of economic dynamics.
Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Improving the tools that model the spatial distributions of SOC stocks at national scales is a priority, both for monitoring change…
Framework isolates causal effects from time series data, improving accuracy under non-stationarity and autocorrelation.
problem Causal inference in non-stationary, autocorrelated time series data.
method Decomposes time series into trend, seasonal, and residual components; performs component-specific causal analysis.
result Framework more accurately recovers ground-truth causal structure than state-of-the-art baselines, especially under strong non-stationarity and temporal autocorrelation.
A new RL framework handles autocorrelated actions for better learning and stability.
problem Improving reinforcement learning algorithms for better stability and efficiency.
method Introduces a new algorithm that optimizes policies with autocorrelated actions.
result The new algorithm outperforms existing methods in four simulated control problems.
Introduces a new Hawkes model with CARMA(p,q) intensity to better model dependence structures.
problem Modeling dependence structures in time series data with realistic autocorrelation functions.
method Develops a Hawkes process with CARMA(p,q) intensity to capture more complex dependencies.
result The CARMA(p,q)-Hawkes model can reproduce more realistic dependence structures and is stationary and positive.
Study of autocorrelation times in neural MCMC simulations for the 2D Ising model.
problem Estimating autocorrelation times in Neural Markov Chain Monte Carlo simulations.
method Analytical and empirical methods to estimate autocorrelation times, proposing new loss functions and training schemes.
result Proposed new loss functions and training schemes that improve autocorrelation times in neural MCMC simulations.
New method improves causal discovery in time series with latent confounders.
problem Low recall in causal discovery for autocorrelated time series with latent confounders.
method Iterative procedure that includes causal parents in conditioning sets, using novel orientation rules.
result Significantly higher recall compared to existing methods, especially in strong autocorrelation cases.
Optimizes portfolio with two controls to minimize trades and maintain signal integrity.
problem Optimizing a single-asset portfolio with transaction costs and signal autocorrelation.
method Formulated an optimization problem to minimize trades while maintaining signal integrity and achieving maximum return.
result Locally optimal solution minimizes trades and achieves maximum return, with a quantifiable improvement based on threshold and autocorrelation removed.
Note: Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelationphysics.chem-ph
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…
Paper optimizes trend-following portfolios using autocorrelation models.
problem Developing an optimal trend-following portfolio strategy.
method Introduces a unifying theoretical setting with autocorrelation models for covariance matrices of trends and risk premia. Specifies practical models for covariance matrices. Decomposes optimal portfolio into four basic components.
result Empirical backtests confirm overperformance of the proposed optimal portfolio.
In this manuscript we analyse the leading statistical properties of fluctuations of (log) 3-month US Treasury bill quotation in the secondary market, namely: probability density function, autocorrelation, absolute values autocorrelation, and absolute values persistency. We verify that this financial instrument, in spit…
Brownian marketsq-fin.ST
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…
A Riemannian Autocorrelation Function and its Application to Non-Local Isoperimetric Energiesmath.AP
Study non-local isoperimetric energies on spheres using a Riemannian autocorrelation function.
problem Analyse non-local isoperimetric energies on spheres.
method Introduce Riemannian autocorrelation function and use it to reformulate and compute the energies.
result Show that the non-local isoperimetric energies can be reformulated and computed using the Riemannian autocorrelation function.
Intra-day variability of the stock market activity versus stationarity of the financial time seriesq-fin.ST
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…
Each year, around 6 million car accidents occur in the U.S. on average. Road safety features (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of road safety features is an important component of safety management systems for…
From turbulence to financial time seriescond-mat.stat-mech
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…