Enhances financial time series forecasting with a multi-period learning framework.
problem Accurate financial time series forecasting requires considering both short-term and long-term trends.
method Proposes a Multi-period Learning Framework (MLF) with three modules: Inter-period Redundancy Filtering, Learnable Weighted-average Integration, and Multi-period self-Adaptive Patching.
result Improves financial time series forecasting accuracy and efficiency.
Algorithm finds periodic points on Veech surfaces.
problem Finding periodic points on non-square-tiled Veech surfaces.
method Developed an algorithm to compute periodic points.
result Proved that in low discriminant, non-square-tiled Veech surfaces have no periodic points, except for fixed points of the Prym involution.
Binary encoding enables neural networks to extrapolate periodic functions.
problem Extrapolating periodic functions without prior knowledge of their form.
method Normalized Base-2 Encoding (NB2E) for continuous numerical values.
result MLPs using NB2E can successfully extrapolate diverse periodic signals.
Algorithm optimally estimates linear dynamical systems with active input selection.
problem Estimating parameters of linear dynamical systems efficiently.
method Active learning with adaptive input selection.
result Finite time bound and asymptotic optimality proven.
Periodic activation functions improve neural network reliability and interpretability.
problem Neural networks reinforce hidden biases, making them unreliable and hard to interpret.
method Introduce periodic activation functions in Bayesian neural networks to establish a connection with stationary Gaussian process priors.
result Periodic activation functions, including sinusoidal, triangular, and ReLU, improve model performance and sensitivity to perturbations.
We propose an algorithm for deciding whether a given braid is pseudo-Anosov, reducible, or periodic. The algorithm is based on Garside's weighted decomposition and is polynomial-time in the word-length of an input braid. Moreover, a reduction system of circles can be found completely if the input is a certain type of r…
Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning. Targeting list is built on predicting physician's sales potential of certain type of patient. In this paper, we present a time-sensitive targeting framework leveraging time series model to predict patient's disease and treat…
A framework for computing holonomy groups of hybrid systems to achieve forward motion.
problem Achieving forward motion from periodic leg motion.
method Developing a framework for computing holonomy groups of hybrid systems.
result Computing holonomy groups of hybrid systems to achieve non-zero net motion.
A new machine learning model uses matrix exponentials for universal approximation.
problem Developing a robust and efficient machine learning model.
method Introduces a novel architecture using matrix exponentials as the only nonlinearity.
result The model achieves universal approximation properties and outperforms other models on benchmark tasks.
Payments data and machine learning improve nowcasting accuracy for macroeconomic indicators.
problem Lagged indicators in linear models are insufficient during crisis periods.
method Non-traditional payments data, nonlinear machine learning, and tailored cross-validation.
result Improved macroeconomic nowcasting accuracy up to 40% during crises.
This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the structure of the input noise distribution by constructing tensors with different types of dimensions. We call this technique Periodic Spatial GAN (PSGAN). The PSGAN has sev…
Meta-learning improves event prediction from short sequences.
problem Predicting events from short sequences is challenging.
method Meta-learning approach using recurrent neural networks and monotonic neural networks.
result Meta-learning enhances long-term prediction performance.
Robust algorithm detects season length without parameters.
problem Automatic detection of season length in time series.
method Interpolating, filtering, detrending followed by autocorrelation analysis.
result Outperforms existing algorithms in practical applications.
It is well-known that the robustness of artificial neural networks (ANNs) is important for their wide ranges of applications. In this paper, we focus on the robustness of the classification ability of a spiking neural network which receives perturbed inputs. Actually, the perturbation is allowed to be arbitrary styles.…
VAIOM models financial returns using continuous input and categorical output.
problem Modeling continuous, noisy, and heterogeneous financial data.
method VAIOM is a decoder-only Transformer that separates input representation from output likelihood.
result VAIOM models outperform fixed single-bar LightGBM baseline in both Test halves.
Sharpe et al. proposed the idea of having an expected utility maximizer choose a probability distribution for future wealth as an input to her investment problem instead of a utility function. They developed a computer program, called The Distribution Builder, as one way to elicit such a distribution. In a single-perio…
Algorithm classifies surface homeomorphisms with polynomial time complexity.
problem Classifying surface homeomorphisms with polynomial time complexity.
method Algorithm to compute curve distances and decide Nielsen-Thurston types.
result Polynomial time classification of surface homeomorphisms.
Study finds Bitcoin market efficient, no exploitable inefficiencies with neural networks.
problem Investigating market inefficiencies in Bitcoin using neural networks.
method Used a feedforward neural network with various asset-related input features.
result Adding more features does not improve prediction accuracy, and one feature set outperforms a buy-and-hold strategy.
A growing part of the behavioral finance literature has addressed some of the stylized facts of financial time series as macroscopic patterns emerging from herding interactions among groups of agents with heterogeneous trading strategies and a limited rationality. We extend a stochastic herding formalism introduced for…
Adaptive MAB algorithms handle composite, anonymous feedback without reward interval knowledge.
problem Multi-armed bandit with composite and anonymous feedback, especially without reward interval size knowledge.
method Proposed adaptive algorithms for stochastic and adversarial cases, without reward interval knowledge.
result First algorithm for adversarial case handling non-oblivious adversary and unknown reward interval size.
This paper introduces hierarchical Gaussian process priors for neural networks to capture weight correlations and inductive biases.
problem Capturing weight correlations and inductive biases in neural networks.
method Hierarchical Gaussian process priors with unit embeddings and input-dependent kernels.
result Hierarchical Gaussian process priors provide competitive predictive performance and desirable uncertainty estimates.
Evaluating AI investment strategies
problem Auditing a black-box algorithmic decision-maker
method Exact decomposition of cumulative regret
result Cumulative regret equals sum of per-period covariances
Study assesses sugar beet yields under EU's neonicotinoids ban and climate change.
problem Impact of yellow virus on sugar beet yields under neonicotinoids ban and climate change.
method Modeling using climate datasets and simulations of aphid flight and abundance.
result Reconstructs sugar beet yields using 'as if' approach without neonicotinoids.
Improved electrical load forecasting model using Fourier-enhanced RNN.
problem Electrical load time series downscaling with high accuracy and low error.
method Combines recurrent neural network with Fourier seasonal embeddings and self-attention.
result Significantly reduces RMSE across different time horizons compared to existing methods.
We introduce the convolutional spectral kernel (CSK), a novel family of non-stationary, nonparametric covariance kernels for Gaussian process (GP) models, derived from the convolution between two imaginary radial basis functions. We present a principled framework to interpret CSK, as well as other deep probabilistic mo…
SGDm with fixed step-size diverges under covariate shift, similar to a parametric oscillator.
problem SGDm with fixed step-size diverges under covariate shift.
method Approximated learning system as a time-varying system of ODEs and characterized divergence/convergence modes.
result SGDm with fixed step-size can diverge under covariate shift, similar to resonance in oscillators.
Geometrically computes sheaves linking HOMFLY-PT homology to Hilbert schemes.
problem Linking HOMFLY-PT homology to geometric structures on Hilbert schemes.
method Geometric sheaf theory, Hochschild homology formality, Hilbert schemes of points.
result Established formalism connecting HOMFLY-PT homology to coherent sheaves on Hilbert schemes.
Paper improves ETF tail-risk monitoring reliability.
problem Unreliable ETF risk monitoring under degraded data.
method Combines quality checks, prediction, scoring, and adjustment.
result Improves tail-risk monitoring, especially during stressed periods.
DDN dynamically combines weights for domain-specific models, improving performance.
problem Limited compute resources for edge devices.
method Domain-aware Dynamic Network (DDN) that combines specialized weights based on input domain.
result DDN achieves up to 2.6% higher AP50 than a static network on the BDD100K benchmark.
Similar to humans and animals, deep artificial neural networks exhibit critical periods during which a temporary stimulus deficit can impair the development of a skill. The extent of the impairment depends on the onset and length of the deficit window, as in animal models, and on the size of the neural network. Deficit…
The paper finds linked periodic orbits in disc homeomorphisms using braids.
problem Finding linked periodic orbits in disc homeomorphisms.
method Interpreting linking of orbits by induced braids and using forcing relations.
result New examples of linked orbits of periods at most 4 for pseudo-Anosov braid types.
Paper proposes a robust framework for detecting multiple periodic components in time series.
problem Detecting multiple periodic components in time series with interlaced patterns and external noise.
method Applying maximal overlap discrete wavelet transform to isolate periodic components, ranking them by wavelet variance, and detecting single periodicity robustly.
result The proposed algorithm outperforms other methods for both single and multiple periodicity detection.
Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes it possible to improve the accuracy of the model for a given amount of reference …
This paper evaluates the impact of the power extent on price in the electricity market. The competitiveness extent of the electricity market during specific times in a day is considered to achieve this. Then, the effect of competitiveness extent on the forecasting precision of the daily power price is assessed. A price…
Nonorientable spanning surfaces of periodic knots can have arbitrarily high first Betti number.
problem Periodic knots do not always have nonorientable spanning surfaces of high genus.
method Examples and calculations of nonorientable spanning surfaces of periodic knots.
result The first Betti number of nonorientable spanning surfaces can be arbitrarily large.
We give criteria for framed links and 3-manifolds to be periodic of prime order. As applications we show that the Poincare sphere is of periodicity 2, 3, 5 only and the Brieskorn sphere (2,3,7) is of periodicity 2, 3, 7 only.
The study classifies isotopy types of 3-periodic nets and their embeddings.
problem Classifying isotopy types of 3-periodic nets and their embeddings.
method Definition of entangled embedded periodic nets, classification methodology using linear graph knots.
result Enumeration and classification of isotopy classes for various 3-periodic nets.
Model for multi-period carbon market pricing with allowances.
problem Carbon market pricing with multiple trading periods and compliance times.
method Singular forward-backward stochastic differential equations (SDEs).
result Value function convergence to infinite period model under certain conditions.
We derive new obstructions to periodicity of classical knots by employing the Heegaard Floer correction terms of the finite cyclic branched covers of the knots. Applying our results to two fold covers, we demonstrate through numerous examples that our obstructions are successful where many existing periodicity obstruct…
Paper defines linking numbers for periodic tangles.
problem Defining invariant linking numbers for periodic tangles.
method Defined linking numbers using motifs and showed invariance.
result Linking numbers are well-defined and invariant.
Study non-parametric frequency-domain system identification from finite samples.
problem Frequency-domain system identification from limited data.
method Empirical Transfer Function Estimate (ETFE) under sub-Gaussian colored noise and stability assumptions.
result ETFE estimates are concentrated around true values with a finite-sample rate of Ntot−1/3 for all frequencies in the H∞ norm. Khovanov-Rozansky homology shows periodic links have group actions.
problem Understanding periodic links through homology.
method Showed Khovanov-Rozansky slN-homology has group actions on m-periodic links. result Proved an analog of periodicity criterion using slN-homology. Maps minimize periodic points for high periods, but not for low periods.
problem Understanding periodic points of maps on infinite type surfaces.
method Analysis of isotopic maps, spun pseudo-Anosov maps, and Handel-Miller maps.
result Spun pseudo-Anosov maps minimize periodic points for sufficiently high periods.
Deep learning predicts VR head movements for better 360-degree videos.
problem Predicting head movements in 360-degree VR videos.
method Deep learning network using position data and video content for long-term prediction.
result 16.1% improvement in prediction accuracy compared to baseline.
We prove the existence of infinitely many periodic points of symplectomorphisms isotopic to the identity if they admit at least one (non-contractible) hyperbolic periodic orbit and satisfy some condition on its flux. The obtained periodic points correspond to periodic orbits whose free homotopy classes are formed by it…
The paper finds a surprising positive correlation between upstreamness and downstreamness in global value chains.
problem The puzzling positive correlation between upstreamness and downstreamness in industries and countries.
method Analysis of a simple model of random Input/Output tables and experiments on empirical data.
result Upstreamness and downstreamness of the same industrial sector/country are positively correlated with a slope close to +1.
Study on periodic knots, proving limitations on their Alexander polynomials.
problem Understanding Alexander polynomials of periodic knots.
method Polynomial factorization, number theory interpretation, computational methods.
result Alexander polynomials of freely periodic knots are restricted to products of cyclotomic polynomials.
Derives an index formula for families of end-periodic Dirac operators.
problem Calculating the index of families of end-periodic Dirac operators.
method Using the renormalized Chern character and Fourier-Laplace transform of the Bismut superconnection.
result Establishes an index formula involving a new end-periodic eta form.