arXiv research

Improved algorithm for optimal stopping problems reduces runtime.

tsflex speeds up time series processing and feature extraction.

BWS selects best window subsets for efficient data pruning.

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…

Change detection in multivariate time series has applications in many domains, including health care and network monitoring. A common approach to detect changes is to compare the divergence between the distributions of a reference window and a test window. When the number of dimensions is very large, however, the naive…

Optimal weight windows are symmetric rectangles centered at peak.

Seglearn is an open-source python package for machine learning time series or sequences using a sliding window segmentation approach. The implementation provides a flexible pipeline for tackling classification, regression, and forecasting problems with multivariate sequence and contextual data. This package is compatib…

RL framework optimizes trading costs in noisy markets.

Differentiable Window improves attention modules by enabling more focused attentions.

Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation …

BAWS adapts window size for financial risk forecasting.

Optimal weight windows are found by projecting the origin onto a convex polytope.

The deep learning trend has recently impacted a variety of fields, including communication systems, where various approaches have explored the application of neural networks in place of traditional designs. Neural networks flexibly allow for data/simulation-driven optimization, but are often employed as black boxes det…

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.

WS-II algorithm segments trajectories with high accuracy.

Developing a range-aware Bayesian optimization framework for discovering diverse designs within target property windows.

Novel time series forecasting method using sliding window signatures.

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 $N_0 \times N_1$ array and $n_0 \times n_1$ 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.

Study predicts cryptocurrency trends using LSTM model.

Optimizes sliding window approach for tracking Gaussian densities.

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 identify 'critical windows' in diffusion models where specific features emerge, providing a theoretical framework.

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 implement a systematic asset allocation model using the Historical Simulation with Flexible Probabilities (HS-FP) framework developed by Meucci. The HS-FP framework is a flexible non-parametric estimation approach that considers future asset class behavior to be conditional on time and market environments, and deriv…

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.

Study uses exchangeable GPs for staggered-adoption policy evaluation in panel data.

Deep Gaussian Processes (DGPs) are hierarchical generalizations of Gaussian Processes that combine well calibrated uncertainty estimates with the high flexibility of multilayer models. One of the biggest challenges with these models is that exact inference is intractable. The current state-of-the-art inference method, …

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…

Measurement noise limits the advantage of nonlinear models over linear models in biomedical prediction

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…

Exploiting the fact that most arrival processes exhibit cyclic behaviour, we propose a simple procedure for estimating the intensity of a nonhomogeneous Poisson process. The estimator is the super-resolution analogue to Shao 2010 and Shao & Lii 2011, which is a sum of $p$ sinusoids where $p$ and the frequency, amplitud…

Enhanced GARCH model uses autoencoder for volatility forecasting.

JAMPR learns to solve complex VRP with time windows.

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…

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.

Deep learning models price convertible bonds with complex reset and call features.

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…

Study improves portfolio optimization for Indonesian banks using robust methods.