A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
We identify 'critical windows' in diffusion models where specific features emerge, providing a theoretical framework.
Study estimates personalized effects of maternal PM2.5 exposure on birth weight.
Study prenatal PM2.5 exposure and 4th grade reading scores, identifying critical windows of susceptibility.
Study on a pinning model with random walk increments, showing convergence to a critical disordered pinning measure.
Shorter time windows and carefully selected features outperform longer periods and extra features in mortgage default prediction.
We present a systematic analysis on the performance of a phonetic recogniser when the window of input features is not symmetric with respect to the current frame. The recogniser is based on Context Dependent Deep Neural Networks (CD-DNNs) and Hidden Markov Models (HMMs). The objective is to reduce the latency of the sy…
This paper explores the dependence modeling of financial assets in a dynamic way and its critical role in measuring risk. Two new methods, called Accelerated Moving Window method and Bottom-up method are proposed to detect the change of copula. The performance of these two methods together with Binary Segmentation \cit…
Optimal weight windows are symmetric rectangles centered at peak.
HAR model outperforms ML in stock forecasting with correct fitting schemes.
There has been renewed recent interest in developing effective lower bounds for Dynamic Time Warping (DTW) distance between time series. These have many applications in time series indexing, clustering, forecasting, regression and classification. One of the key time series classification algorithms, the nearest neighbo…
Differentiable Window improves attention modules by enabling more focused attentions.
The year 2017 saw the rise and fall of the crypto-currency market, followed by high variability in the price of all crypto-currencies. In this work, we study the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the phenomenon of critical slowing down) or the stochastic t…
CSP improves time-series forecasting without training, outperforming DeepNPTS in speed and accuracy.
We demonstrate the application of an algorithmic trading strategy based upon the recently developed dynamic mode decomposition (DMD) on portfolios of financial data. The method is capable of characterizing complex dynamical systems, in this case financial market dynamics, in an equation-free manner by decomposing the s…
BAWS adapts window size for financial risk forecasting.
Optimal weight windows are found by projecting the origin onto a convex polytope.
This paper investigates bias in resampled backtests for financial portfolios, finding it often negligible.
Five simple soft sensor methodologies with two update conditions were compared on two experimentally-obtained datasets and one simulated dataset. The soft sensors investigated were moving window partial least squares regression (and a recursive variant), moving window random forest regression, the mean moving window of…
Paper introduces a differentiable STFT for continuous window length optimization.
Novel time series forecasting method using sliding window signatures.
Time series forecasting with limited data is a challenging yet critical task. While transformers have achieved outstanding performances in time series forecasting, they often require many training samples due to the large number of trainable parameters. In this paper, we propose a training technique for transformers th…
Auto-regressive models improve smoothing efficiency with exponentially tapered windows.
Proposes a sliding window method for better portfolio trading.
We present a new algorithm for the 2D Sliding Window Discrete Fourier Transform (SWDFT). Our algorithm avoids repeating calculations in overlapping windows by storing them in a tree data-structure based on the ideas of the Cooley- Tukey Fast Fourier Transform (FFT). For an array and wi…
Improved convergence of fixed-point methods using windowed Anderson acceleration.
Improved Granger causality method for dynamic time series data.
WeldNet reduces complex dynamics to simpler, manageable segments.
In many applications, monitoring area under the ROC curve (AUC) in a sliding window over a data stream is a natural way of detecting changes in the system. The drawback is that computing AUC in a sliding window is expensive, especially if the window size is large and the data flow is significant. In this paper we propo…
Study predicts cryptocurrency trends using LSTM model.
While social networks can provide an ideal platform for up-to-date information from individuals across the world, it has also proved to be a place where rumours fester and accidental or deliberate misinformation often emerges. In this article, we aim to support the task of making sense from social media data, and speci…
Optimizes sliding window approach for tracking Gaussian densities.
BWS selects best window subsets for efficient data pruning.
WS-II algorithm segments trajectories with high accuracy.
Occupant behavior (OB) and in particular window openings need to be considered in building performance simulation (BPS), in order to realistically model the indoor climate and energy consumption for heating ventilation and air conditioning (HVAC). However, the proposed OB window opening models are often biased towards …
We study the dynamics of the linear and non-linear serial dependencies in financial time series in a rolling window framework. In particular, we focus on the detection of episodes of statistically significant two- and three-point correlations in the returns of several leading currency exchange rates that could offer so…
We introduce a model-independent approximation for the branching ratio of Hawkes self-exciting point processes. Our estimator requires knowing only the mean and variance of the event count in a sufficiently large time window, statistics that are readily obtained from empirical data. The method we propose greatly simpli…
New framework learns policies for partially observable systems.
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…
Proposes a method to train classifiers with delayed feedback using a time window.
Statistical test verifies long-term rating system calibration with overlapping time windows.
CrossAD detects anomalies in time series data by considering cross-scale associations and cross-window modeling.
K-fold Cross Validation is commonly used to evaluate classifiers and tune their hyperparameters. However, it assumes that data points are Independent and Identically Distributed (i.i.d.) so that samples used in the training and test sets can be selected randomly and uniformly. In Human Activity Recognition datasets, we…
New algorithms achieve optimal regret in sliding window model with limited memory.
This paper addresses the question of identifying the time-window in short-term past from which the information regarding the future occupant's window opening actions and resulting window states in buildings can be predicted. The addressed sequence duration was in the range between 30 and 240 time-steps of indoor climat…
JAMPR learns to solve complex VRP with time windows.
We consider reinforcement learning in changing Markov Decision Processes where both the state-transition probabilities and the reward functions may vary over time. For this problem setting, we propose an algorithm using a sliding window approach and provide performance guarantees for the regret evaluated against the op…
Deep learning solves EV routing with time windows for EV fleets.
This study proposes a trainable adaptive window switching (AWS) method and apply it to a deep-neural-network (DNN) for speech enhancement in the modified discrete cosine transform domain. Time-frequency (T-F) mask processing in the short-time Fourier transform (STFT)-domain is a typical speech enhancement method. To re…