Study compares Bitcoin, gold, and gas price complexity using multifractal and multiscale entropy methods.
problem Quantifying complexity of financial time series for market analysis.
method Employed MF-DFA and RCMSE to analyze Bitcoin, GBP/USD, gold, and natural gas price log-return time series.
result Bitcoin shows higher complexity compared to other markets, linked to higher nonlinear correlations.
Paper introduces MN-DAG for modeling evolving causal relationships in multivariate time series.
problem Modeling causal relationships that evolve over time and occur at different scales.
method Probabilistic generative model based on spectral and causality theories, combined with Bayesian stochastic variational inference.
result MN-CASTLE outperforms baseline models in identifying causal relationships in multivariate time series data.
Study on deep and wide echo state networks for forecasting complex time series.
problem Performance analysis of deep reservoir computing models.
method Investigates the impact of partitioning neurons and parallel pathways on forecasting accuracy.
result Wide and deep networks outperform shallow models in forecasting multiscale spatiotemporal data.
UniShape improves time series classification by selecting relevant subsequences.
problem Classifying time series data requires capturing interpretable shapelets.
method UniShape uses a shape-aware adapter to aggregate multiscale subsequences into class tokens.
result UniShape achieves state-of-the-art classification performance.
MS-CASTLE learns causal structures across multiple time scales.
problem Inferring causal relationships between time series data at different scales.
method Uses stationary wavelet transform and non-convex optimization to estimate causal structures.
result MS-CASTLE reveals meaningful causal interactions, especially at mid-term time resolutions.
Study shows increased precipitation variability in Paris area over years.
problem Evaluating the evolution of precipitation variability over time.
method Shape-based Dynamic Time Warping (IMS-DTW) for clustering rainfall time series.
result Precipitation variability increased in Paris area over years.
Study uses multifractal detrended cross-correlation to detect Forex arbitrage opportunities.
problem Detecting arbitrage opportunities in Forex markets.
method Multifractal detrended cross-correlation analysis applied to Forex time series.
result Strong cross-correlations found between exchange rates involved in triangular relations, including AUD and NZD.
Recent advancements in recurrent neural network (RNN) research have demonstrated the superiority of utilizing multiscale structures in learning temporal representations of time series. Currently, most of multiscale RNNs use fixed scales, which do not comply with the nature of dynamical temporal patterns among sequences…
Paper separates financial time series into fast and slow components.
problem Multiscale behavior in financial time series data.
method Uses variance and tail stationarity criteria as generalized eigenvalue problems.
result Identifies slow and fast components in asset returns and prices.
SRMD uses random features for efficient time-frequency analysis.
problem Efficiently analyzing time-series data with low computational cost.
method Sparse Random Mode Decomposition (SRMD) constructs a sparse approximation to the spectrogram.
result SRMD outperforms other methods in signal representation, outlier removal, and mode decomposition.
TimeMixer predicts global financial asset volatility, excelling in short-term forecasts.
problem Predicting volatility in global financial markets is challenging due to complexity and non-linear dynamics.
method Uses TimeMixer, a multiscale-mixing model for forecasting across different scales.
result TimeMixer performs exceptionally well in short-term volatility forecasting but less so in longer-term predictions.
Researchers have studied the first passage time of financial time series and observed that the smallest time interval needed for a stock index to move a given distance is typically shorter for negative than for positive price movements. The same is not observed for the index constituents, the individual stocks. We use …
This paper investigates the hedging effectiveness of a dynamic moving window OLS hedging model, formed using wavelet decomposed time-series. The wavelet transform is applied to calculate the appropriate dynamic minimum-variance hedge ratio for various hedging horizons for a number of assets. The effectiveness of the dy…
Study cryptocurrency price dynamics using adaptive EMD and spectral analysis.
problem Analyze the time-varying volatility of cryptocurrency prices.
method Adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis.
result Reveal the properties of various timescales in cryptocurrency price dynamics.
Trading strategies improved by classifying financial time-series images.
problem Improving financial trading strategies using image classification.
method Created a dataset of financial time-series images, labeled them, and trained machine learning models.
result Machine learning models trained on image data outperformed traditional time-series analysis.
Proposes a new algorithm for non-stationary bandits.
problem Non-stationary reward distributions in contextual bandits.
method Multiscale changepoint detection for adaptive learning.
result Regret bound analysis and superior performance in experiments.
The dynamics of prices in financial markets has been studied intensively both experimentally (data analysis) and theoretically (models). Nevertheless, a complete stochastic characterization of volatility is still lacking. What it is well known is that absolute returns have memory on a long time range, this phenomenon i…
Bayesian model learns multiscale interactions in complex systems.
problem Understanding dynamic interplay between processes at different time scales.
method Bayesian learning framework with Particle Gibbs with Ancestor Sampling (PGAS) algorithm.
result Demonstrated the effectiveness of the proposed approach through simulations.
Paper combines geometry and time-series analysis for spatiotemporal data.
problem Multivariate time-series data from multiple sensors.
method Combines manifold learning, Riemannian geometry, and spectral analysis.
result Proposes Riemannian multi-resolution analysis (RMRA) for dynamic mode extraction.
Neural network approach simplifies multiscale problem homogenization.
problem Homogenizing multiscale problems with varying microscale structures.
method Derivative-free neural network with Brownian walkers.
result Neural network method is computationally efficient and robust.
New approach combines PCA and t-sne for better data analysis.
problem Multiscale complexity in high-dimensional data.
method Multiscale joint characterization using PCA and t-sne.
result Joint characterization detects signals not seen by PCA or t-sne alone.
This paper proposes a novel multiscale estimator for the integrated volatility of an Ito process, in the presence of market microstructure noise (observation error). The multiscale structure of the observed process is represented frequency-by-frequency and the concept of the multiscale ratio is introduced to quantify t…
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.
New framework for analyzing hydroclimatic time series across multiple scales.
problem Understanding geophysical processes and evaluating stochastic models across different time scales.
method A novel feature compilation method for multi-scale hydroclimatic analyses.
result Identified similarities and differences in time series types across various temporal resolutions.
Study the link between entropy and market efficiency using fractal properties.
problem Determining market efficiency using entropy-based measures and fractal properties.
method Theoretical expression for market information using fractional Brownian motion and Lamperti transform. Multiscale method to interpret entropy and market information.
result A Hurst exponent close to 1/2 can lead to high informativeness of time series due to stationarity.
This study applies EMD to MSCI World index and converts IMFs into graphs for GNN modeling.
problem Modeling financial time series with GNNs.
method EMD, CEEMDAN, graph transformations (natural visibility, horizontal visibility, recurrence, transition graphs), topological analysis.
result High-frequency IMFs yield dense, highly connected small-world graphs; low-frequency IMFs produce sparser networks.
The paper provides an efficient method to price path-dependent derivatives using multiscale stochastic volatility models.
problem Pricing path-dependent derivatives under multiscale stochastic volatility models.
method Derives a Malliavin representation for the first-order approximation of the price of path-dependent derivatives.
result An efficient Monte Carlo approximation for pricing path-dependent derivatives is derived.
New algorithm learns switching dynamics from multiple neural signals.
problem Learning accurate switching dynamical system models from multimodal neural data.
method Unsupervised learning algorithm for multiscale switching dynamical system models.
result Switching multiscale dynamical system models outperform single-scale models in behavior decoding.
In the present work we investigate the multiscale nature of the correlations for high frequency data (1 minute) in different futures markets over a period of two years, starting on the 1st of January 2003 and ending on the 31st of December 2004. In particular, by using the concept of "local" Hurst exponent, we point ou…
Study optimal futures trading strategies for assets with multiscale central tendency price model.
problem Optimal dynamic trading of futures with multiscale central tendency price model.
method Derive no-arbitrage futures prices, solve HJB equations for optimal strategies.
result Optimal trading strategies depend on asset parameters and futures risk premia.
Deterministic GD can behave stochastically in large learning rates for multiscale functions.
problem Understanding deterministic GD's stochastic behavior in large learning rates for multiscale objectives.
method Established a sufficient condition for deterministic GD to converge to a rescaled Gibbs distribution in large learning rates for multiscale functions.
result Deterministic GD can converge to a statistical distribution in large learning rates for multiscale functions.
In recent years a new type of tradable assets appeared, generically known as cryptocurrencies. Among them, the most widespread is Bitcoin. Given its novelty, this paper investigates some statistical properties of the Bitcoin market. This study compares Bitcoin and standard currencies dynamics and focuses on the analysi…
In the framework of Multifractal Diffusion Entropy Analysis we propose a method for choosing an optimal bin-width in histograms generated from underlying probability distributions of interest. The method presented uses techniques of Rényi's entropy and the mean squared error analysis to discuss the conditions under whi…
Discusses MultiFIT for multivariate dependence, comparing it to HSIC tests.
problem Comparing Multiscale Fisher's Independence Test (MultiFIT) to HSIC tests for multivariate dependence.
method Compares MultiFIT to HSIC tests, highlighting exact level control and performance limitations.
result Observes performance limitations of MultiFIT in terms of test power.
The paper proves Gorenstein contractions for multiscale differentials on nodal curves.
problem Proving Gorenstein contractions for multiscale differentials on nodal curves.
method Addressing the conjecture by Ranganathan and Wise, showing contractions level by level.
result Multiscale differentials can be contracted to Gorenstein singularities, level by level, from the top down.
A new sampling method estimates scores without training or nested MCMC.
problem Efficient sampling from complex, unnormalised distributions.
method Multiscale averaging in SDEs for score estimation.
result Empirical results show competitive accuracy and efficiency.
Framework models multiscale dynamics with Bayesian learning for regime changes.
problem Analyzing complex interactions between fast and slow processes.
method Hierarchical state-space modeling with Sequential Monte Carlo.
result Bayesian approach accurately tracks state transitions and identifies switching dynamics.
The paper tackles physical constraints in probabilistic machine learning for CG models of high-dimensional systems.
problem Introducing physical constraints in probabilistic machine learning objectives for coarse-graining dynamical systems.
method Formulating coarse-graining process using probabilistic state-space model and accounting for constraints as virtual observables.
result Probabilistic inference tools can identify coarse-grained variables without needing a fine-to-coarse projection or time-derivatives.
We study the multiscale simplicial flat norm (MSFN) problem, which computes flat norm at various scales of sets defined as oriented subcomplexes of finite simplicial complexes in arbitrary dimensions. We show that the multiscale simplicial flat norm is NP-complete when homology is defined over integers. We cast the mul…
Generative framework learns effective, lower-dimensional models from high-dimensional data.
problem Predicting long-term behavior of complex, multiscale systems with limited data.
method Physics-aware probabilistic model order reduction with latent variables.
result Guaranteed long-term stability and predictive accuracy in multiscale physical systems.
Graph clustering uses multiscale community detection for improved performance.
problem Improving data clustering accuracy and robustness.
method Graph-theoretical approach combining multiscale community detection.
result Multiscale graph-based clustering achieves better performance than traditional methods.
We study, both analytically and numerically, an ARCH-like, multiscale model of volatility, which assumes that the volatility is governed by the observed past price changes on different time scales. With a power-law distribution of time horizons, we obtain a model that captures most stylized facts of financial time seri…
This paper examines multifractal dynamics in cryptocurrencies using two methodologies.
problem Understanding the multifractal nature of cryptocurrencies and their stochastic processes.
method Two alternative multi-scaling methodologies applied to 84 cryptocurrencies.
result Cryptocurrencies exhibit different degrees of long-range dependence and stochastic processes.
DMGNN predicts 3D human motions using adaptive multiscale graphs.
problem Predicting 3D skeleton-based human motions accurately.
method Dynamic multiscale graph neural networks (DMGNN) with adaptive multiscale graphs and MGCU.
result DMGNN outperforms state-of-the-art methods in short and long-term predictions.
Study analyzes cryptocurrency market complexity, comparing it to traditional markets.
problem Understanding the dynamics and characteristics of cryptocurrency markets.
method Statistical physics methods and analysis of price fluctuations.
result Cryptocurrency market exhibits complexity similar to traditional markets but with slower information flow.
Paper proposes a new adaptive multiscale value function approximation for reinforcement learning.
problem Value function approximation in reinforcement learning with varying complexity.
method Adaptive multiscale approximation using multiresolution analysis and tree approximation.
result Convergence rate of the multiscale approximation is independent of basis function regularity.
A framework learns multiscale dynamics from single trajectories using normalizing flows.
problem Learning effective stochastic dynamics from single observed paths of slow variables.
method Data-driven approach based on coupled multiscale SDEs, stochastic averaging, and normalizing flows for density modeling.
result Scalable approach to capturing epistemic uncertainty in multiscale systems.
The paper develops a physics-aware method for modeling multiscale dynamics with reduced data.
problem Discovering effective, lower-dimensional models for high-dimensional dynamical systems.
method Probabilistic deep neural networks incorporating physical constraints.
result The method reduces the need for extensive multiscale simulations (Small Data regime).