Study how financial market participants have different objectives at various time scales.
problem Understanding how financial market participants have different objectives at various time scales.
method Use Inverse Reinforcement Learning to compute the effective reward function for the aggregate agent class at each scale.
result Identify differences in reward functions for feature vectors across different scales, indicating different objectives of market participants.
EMD reveals dynamic cross-correlations across financial indices at various time-scales.
problem Characterizing time-varying multidimensional cross-correlations in financial indices.
method Empirical Mode Decomposition applied to intraday time series of financial indices.
result Uncovered rich heterogeneity of interactions dependent on time-scale and led-lag relations.
Analyzed US firm data 1970-2019, identifying scale effects and distributional forms.
problem Understanding differences between small and large firms over time.
method Examined all public US firms, used stylized facts and DLN distribution analysis.
result Small firms are systematically different from large firms, with scale-dependent heteroskedasticity.
Volatility of intra-day stock market indices computed at various time horizons exhibits a scaling behaviour that differs from what would be expected from fractional Brownian motion (fBm). We investigate this anomalous scaling by using empirical mode decomposition (EMD), a method which separates time series into a set o…
A new method for reinforcement learning scales errors without tuning.
problem Error scaling varies across reinforcement learning tasks and stages.
method A simple scaling mechanism for temporal-difference learning.
result The method effectively mitigates interference between learning tasks.
Study compares different scoring rules for machine-learned weather forecasts, finding scale-awareness improves forecast realism.
problem Improving the accuracy of machine-learned probabilistic weather forecasts.
method Comparison of scoring rules (CRPS, fair global energy score, graph energy score) and analysis of their impact on forecast field spectra.
result Scale-awareness improves forecast realism, particularly in the tropics.
Method identifies multi-scale behavioral motifs in rat locomotion.
problem Challenging to identify behavioral motifs across different time scales.
method Preprocessing and parallel HMM training for different scales.
result Rat locomotion is composed of distinct motifs from second to minute scale.
AAANE embeds networks by learning attention weights for multi-scale structure.
problem Existing methods ignore the role of different scales in network embedding.
method AAANE uses an attention-based adversarial autoencoder to learn robust representations.
result AAANE outperforms existing methods on real-world networks.
We make use of wavelet transform to study the multi-scale, self similar behavior and deviations thereof, in the stock prices of large companies, belonging to different economic sectors. The stock market returns exhibit multi-fractal characteristics, with some of the companies showing deviations at small and large scale…
In addressing the question of the time scales characteristic for the market formation, we analyze high frequency tick-by-tick data from the NYSE and from the German market. By using returns on various time scales ranging from seconds or minutes up to two days, we compare magnitude of the largest eigenvalue of the corre…
Improved average distance classifier for HDLSS settings with multiple population differences.
problem Poor performance of average distance classifier in HDLSS settings with location and scale differences.
method Proposed transformations to the average distance classifier to handle multiple population differences.
result The proposed classifiers perform well even when populations differ in other aspects than location and scale.
AutoScale improves LLM pre-training by adjusting data mixtures at different scales.
problem Data mixtures that work well at small scales may not perform as well at larger scales.
method AutoScale uses a two-stage approach: fitting a model to predict loss under different compositions and extrapolating optimal compositions to larger scales.
result AutoScale accelerates convergence and improves downstream performance.
Proposes a method to predict time series data using scale information.
problem Sequential data exhibits different behavior at different scales.
method Scale-based inference using k-nearest neighbors and sequential prediction. result Asymptotically, the algorithm produces the best prediction error.
Scaling algorithms improve joint training of deep energy-based models.
problem Joint training of deep energy-based models often fails and delivers worse results.
method Proposed online and offline scaling algorithms to fix joint training.
result Scaling algorithms improve joint training and deliver better results.
ASRNN adapts scales dynamically for better sequence modeling.
problem Fixed scales in multiscale RNNs don't match temporal patterns.
method Adaptively learns and adjusts scales based on temporal contexts.
result ASRNNs yield better performances with dynamical scaling.
This thesis tackles data fusion issues across different biological scales and types.
problem Heterogeneity in data types and scales in systems biology.
method Developed statistical methods to fuse heterogeneous data sets.
result Advantages of proposed methods assessed through simulations and real data analysis.
This paper examines the volatility and covariance dynamics of cash and futures contracts that underlie the Optimal Hedge Ratio (OHR) across different hedging time horizons. We examine whether hedge ratios calculated over a short term hedging horizon can be scaled and successfully applied to longer term horizons. We als…
A new model classifies lightning signals more accurately across different scales.
problem Classifying VLF lightning transients to reduce interference and improve navigation system reliability.
method Introduces a multi-scale residual transformer (MRTransformer) to classify lightning signals.
result Achieved 90% accuracy in lightning signal classification.
A method based on wavelet transform and genetic programming is proposed for characterizing and modeling variations at multiple scales in non-stationary time series. The cyclic variations, extracted by wavelets and smoothened by cubic splines, are well captured by genetic programming in the form of dynamical equations. …
Study analyzes price response and spread impact in foreign exchange markets.
problem Understanding deviations from Markovian behavior in foreign exchange markets.
method Detailed large-scale data analysis of price response functions for different years and time scales, using pip bid-ask spread definition.
result Large pip spreads significantly impact price response in foreign exchange markets.
We empirically analyze the scaling properties of daily Foreign Exchange rates, Stock Market indices and Bond futures across different financial markets. We study the scaling behaviour of the time series by using a generalized Hurst exponent approach. We verify the robustness of this approach and we compare the results …
The paper predicts loss scaling across different datasets and compute scales.
problem Predicting loss scaling across different datasets and compute scales.
method Derive shifted power law relationships between train and test losses.
result Shifted power law relationships hold for various datasets and tasks, improving prediction accuracy.
Study tests rough fractional volatility model across different time scales, revealing new volatility patterns.
problem Testing robustness of rough fractional volatility model over various time scales.
method Used large dataset on FX rates, included smoothing and measurement errors, analyzed log-log plots of realized variance increments.
result Found new stylized facts in volatility patterns, including convexity and nonlinear behavior.
CrossAD detects anomalies in time series data by considering cross-scale associations and cross-window modeling.
problem Anomaly detection in time series data is challenging due to varying patterns at different scales and fixed window sizes.
method CrossAD incorporates cross-scale reconstruction and a query library to capture dynamic cross-scale associations and comprehensive context.
result CrossAD achieves state-of-the-art performance in anomaly detection across multiple real-world datasets.
We propose a framework combining detrended fluctuation analysis with standard regression methodology. The method is built on detrended variances and covariances and it is designed to estimate regression parameters at different scales and under potential non-stationarity and power-law correlations. The former feature al…
Enhances speech emotion recognition by adapting to varying time scales.
problem Robust emotion recognition from speech audio with temporal variations.
method Introduces multi-time-scale (MTS) convolutional layers to CNNs.
result MTS layers improve generalization, especially on smaller datasets.
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…
Automated spectral clustering algorithm discovers clusters without parameter tuning.
problem Automatic spectral clustering for multi-scale data.
method Heuristic iterative eigengap search with global and local scaling.
result Discover different patterns with accuracy >90% in most cases.
Scaling feature values is an important step in numerous machine learning tasks. Different features can have different value ranges and some form of a feature scaling is often required in order to learn an accurate classifier. However, feature scaling is conducted as a preprocessing task prior to learning. This is probl…
Event detection has been one of the most important research topics in social media analysis. Most of the traditional approaches detect events based on fixed temporal and spatial resolutions, while in reality events of different scales usually occur simultaneously, namely, they span different intervals in time and space…
Improves U-Net for scale equivariance in semantic segmentation.
problem Improving generalization in semantic segmentation tasks with varying scales.
method Introduces Scale Equivariant U-Net (SEU-Net) with carefully applied subsampling and upsampling layers and scale-equivariant layers.
result Significantly improved generalization to different scales compared to U-Net and scale-equivariant architecture without upsampling.
Investment strategies differ based on short-term and long-term market time scales.
problem Identifying and understanding different time scales in stock market dynamics.
method Empirical Mode Decomposition (EMD) and Hurst Exponent analysis.
result Short-term market dynamics are random, while long-term are correlated with company fundamentals.
We establish decoupled functional CLTs for two-time-scale stochastic approximation.
problem Understanding the asymptotic behavior of two-time-scale stochastic approximation.
method Martingale problem approach and auxiliary sequence.
result The limiting dynamics of two-time-scale SA are independent of each other.
Theory explains neural network scaling with dataset and model size.
problem Neural network scaling laws with dataset and model size.
method Identified variance-limited and resolution-limited scaling behaviors.
result Four scaling regimes explained: infinite data, infinite width, resolution-limited, and large width.
A new robust scaling approach improves downstream metabolomics analysis.
problem Challenges in choosing scaling techniques for metabolomics data.
method Introduces a weighted scaling approach robust to outliers.
result The proposed method outperforms traditional scaling techniques in both outlier-free and outlier-present datasets.
A new method, tree-SNE, solves the scale problem in t-SNE.
problem Clustering and visualizing high-dimensional data, especially MNIST digits.
method Revisits t-SNE idea to create a 2+1 dimensional embedding with a scale parameter.
result The optimal embedding depends continuously on the scale parameter for all initial conditions.
Adaptive loss scaling speeds up and improves deep learning training.
problem Numerical underflow in mixed precision training.
method Adaptive loss scaling that automatically computes layer-wise loss scale values during training.
result Adaptive loss scaling leads to shorter convergence time and improved accuracy.
Proposes a new two-stage scaling method for data preprocessing.
problem Data scaling in statistical learning models.
method Two-stage scaling method: linear regression fitting followed by data scaling.
result Advantages of the new scaling method demonstrated through simulations and real data analysis.
A new approach to group fairness treats it as a bargaining problem.
problem Fairness in deploying predictors across subpopulations.
method Interpreting fairness as a bargaining problem and proposing relative improvement.
result Relative improvement provides axiomatic justification and finite-sample convergence guarantees.
We measure the influence of different time-scales on the dynamics of financial market data. This is obtained by decomposing financial time series into simple oscillations associated with distinct time-scales. We propose two new time-varying measures: 1) an amplitude scaling exponent and 2) an entropy-like measure. We a…
New framework for understanding infinite-width neural networks.
problem Understanding the infinite-width limit behavior of neural networks.
method General framework to study limit behavior of neural models based on hyperparameter scaling.
result Derives scaling for existing mean-field and neural tangent kernel limits and introduces new dynamically stable limits.
New method selects diffusion scales for graph wavelets.
problem Choosing optimal diffusion scales for graph wavelets.
method Proposes an unsupervised method using information theory.
result Method selects diffusion scales for graph wavelets.
We analyze the Hessian spectra of large models up to 100B parameters.
problem Accurate Hessian spectra of large foundation models are difficult to obtain.
method We use shard-local finite-difference Hessian vector products and stochastic Lanczos quadrature.
result We produce the first large-scale spectral density estimates of foundation models.
MSG-GAN stabilizes GANs by passing gradients at multiple scales.
problem Stability and adaptability of GANs to different datasets.
method Introducing MSG-GAN with multi-scale gradient flow.
result MSG-GAN converges stably on various image datasets and loss functions.
This paper improves traditional Markowitz optimization by considering variance at multiple time scales.
problem Traditional Markowitz optimization limits to a single time scale, ignoring variance across different frequencies.
method Introduces multifrequency optimization allowing specification of target Hurst exponents across multiple time scales.
result Effective risk management strategy that aligns with investor preferences at various time scales.
Space2Vec learns multi-scale spatial representations from grid cell insights.
problem Encoding spatial features with varying scales from GIS data.
method Proposes Space2Vec, a multi-scale representation learning model using grid cell insights.
result Space2Vec outperforms baselines in predicting POI types and image classification with geo-locations.
Financial markets can be described on several time scales. We use data from the limit order book of the London Stock Exchange (LSE) to compare how the fluctuation dominated microstructure crosses over to a more systematic global behavior.
New method disentangles sources of different timescales in planetary seismic data.
problem Unsupervised source separation of multi-scale seismic data from planetary missions.
method Wavelet scattering spectra for multi-scale clustering and variational autoencoder for source separation.
result Disentangles sources with different timescales in InSight mission seismic data.