Paper introduces a differentiable STFT for continuous window length optimization.
problem Optimizing window length in spectrograms for neural networks.
method Defines a differentiable short-time Fourier transform with continuous window length.
result Demonstrates improved performance in estimation and classification tasks.
This study optimizes trading strategy parameters using walk-forward techniques and finds robust performance.
problem Optimizing trading strategy performance through parameter optimization.
method Walk-forward optimization with varying window lengths, tested on Bitcoin, Binance Coin, and Ethereum.
result The strategy outperforms Buy-and-Hold with lower drawdown and higher Information Ratio.
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…
Study predicts cryptocurrency trends using LSTM model.
problem Predicting cryptocurrency price trends.
method Combination of window-sliding and prediction range method with LSTM model.
result Established model for cryptocurrency price trend prediction.
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…
Graph Pointer Networks and hierarchical reinforcement learning solve combinatorial optimization problems like TSP.
problem Traveling Salesman Problem (TSP) with constraints.
method Graph Pointer Networks (GPNs) and hierarchical reinforcement learning.
result GPNs and hierarchical RL find optimal solutions for TSP and TSP with time windows.
In the paper, we introduce a new measure of correlation between possibly non-stationary series. As the measure is based on the detrending moving-average cross-correlation analysis (DMCA), we label it as the DMCA coefficient ρDMCA(λ) with a moving average window length λ. We analytically show that the coefficient…
Optimal weight windows are symmetric rectangles centered at peak.
problem Finding the best weight windows for weighted least squares.
method Investigated symmetric and tapered rectangle window weights, showing the best rectangle window is optimal.
result The best rectangle window is optimal for all tapered rectangle window definitions.
New algorithm optimizes resource allocation in non-stationary networks.
problem Optimal resource allocation in non-stationary RMABs is computationally hard.
method Sliding-Window Online Whittle (SW-Whittle) policy for non-stationary transition kernels.
result Sub-linear dynamic regret achieved with unknown variation budget.
Shorter time windows and carefully selected features outperform longer periods and extra features in mortgage default prediction.
problem The paradox of increased training data and features leading to worse model performance in time series prediction.
method Empirical study using Fannie Mae's mortgage data, comparing different time window lengths and feature combinations.
result Shorter time windows and carefully selected features yield superior prediction results in mortgage default prediction.
Adaptive beamforming collapses in highly non-stationary environments, but the Universal Switching Beamformer resolves this by dynamically adjusting memory length.
problem Adaptive beamforming performance degrades in highly non-stationary environments.
method Integrating sequential prediction into the beamforming architecture.
result The USB achieves agility and precision in tracking highly non-stationary scenes.
This paper presents a novel adaptive-filter approach for predicting assets on the stock markets. Concepts are introduced here, which allow understanding this method and computing of the corresponding forecast. This approach is applied, as an example, through the prediction over the actual valuation of the PETR3 shares …
ALT transforms time series data for better classification.
problem Efficiently classifying time series data with varying temporal scales.
method ALT algorithm using variable-length shifted time windows.
result State-of-the-art performance with minimal computational overhead.
Improved algorithm reduces excess risk in selective learning.
problem Selective learning with windowed model selection.
method Hybrid Exponential Weights Algorithm and bounded-recall ERM.
result Achieves expected excess risk of O((log log |L| + log log n) / log n).
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…
ALT improves TSC by capturing complex patterns in time series data.
problem Challenges in traditional TSC methods with time series complexity and variability.
method ALT incorporates variable-length shifted time windows to enhance LLT for better feature representation.
result ALT achieves state-of-the-art performance with few hyperparameters.
We apply the Hurst exponent idea for investigation of DJIA index time-series data. The behavior of the local Hurst exponent prior to drastic changes in financial series signal is analyzed. The optimal length of the time-window over which this exponent can be calculated in order to make some meaningful predictions is di…
SummerTime summarizes variable-length time series for machine learning applications.
problem Classical machine learning methods struggle with variable-length time series data.
method Summarizes time series into a fixed-length feature vector using Gaussian Mixture Models (GMM).
result Improves classification and regression performance in physical activity analysis.
Proposes a sliding window method for better portfolio trading.
problem Log-optimal portfolio problem with time-varying weights.
method Data-driven sliding window approach to solve log-optimal portfolio problem.
result Trading strategy outperforms classical log-optimal portfolio in cumulative returns.
Improved stock price prediction using LSTM with customized loss function and analyst calls.
problem Forecasting stock prices using LSTM neural networks.
method Customized LSTM model with improved loss function, analyst calls integration, and attention units.
result Improved performance of LSTM model over ARIMA for stock price prediction.
The paper tackles long-context linear system identification with improved sample complexity bounds.
problem Identifying dynamical systems with long dependencies over fixed context windows.
method Established sample complexity bounds for systems with linear dependencies over a context window of length p.
result The learning process is not hindered by slow mixing properties in extended context windows.
Optimal weight windows are found by projecting the origin onto a convex polytope.
problem Finding the best weight windows for a weighted moving average smoother.
method Formulated as a quadratic program and projection onto a convex polytope.
result Optimal weight windows are symmetrical and decrease in weight away from the center.
The paper studies knot densities under various constraints and degenerations.
problem Understanding knot densities under different constraints and their degenerations.
method Introduces and analyzes unconstrained and ropelength-windowed p-densities of knot types. result The degenerations in the unconstrained theory and the introduction of ropelength-windowed densities.
Optimizes sliding window approach for tracking Gaussian densities.
problem Improving tracking performance of Gaussian density estimation.
method Theoretical analysis of sliding window Gaussian Kernel Density Estimators.
result Empirical evidence shows improved tracking performance with optimal weight sequence.
We consider the problem of predicting the next observation given a sequence of past observations, and consider the extent to which accurate prediction requires complex algorithms that explicitly leverage long-range dependencies. Perhaps surprisingly, our positive results show that for a broad class of sequences, there …
Study improves portfolio optimization for Indonesian banks using robust methods.
problem Uncertainty in historical return and risk estimates leads to suboptimal portfolios.
method Robust optimization with moving-window and bootstrapping methods.
result Moving-window method with smaller risk-aversion parameter provides better risk-return trade-off.
Paper tests for time-varying entropy in stock prices, finding periods of inefficiency.
problem Testing for time-varying entropy in stock price dynamics.
method Unbiased approximation of Shannon entropy variance, optimal rolling window selection, hypothesis testing.
result Existence of periods of market inefficiency for meme stocks.
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…
New algorithms achieve optimal regret in sliding window model with limited memory.
problem Experts problem in the sliding window model with limited information.
method 2 queries, polylog(nT) memory, exponential improvement on memory.
result Achieve optimal regret of sqrt(nW)polylog(nT) with 2 queries and polylog(nT) memory.
Traders adopt different trading strategies to maximize their returns in financial markets. These trading strategies not only results in specific topological structures in trading networks, which connect the traders with the pairwise buy-sell relationships, but also have potential impacts on market dynamics. Here, we pr…
Bi-GAN model for imputing and predicting irregular time-series data.
problem Irregularly observed, varying length time-series data with missing entries.
method Bi-GAN model using a bidirectional recurrent network in a generative adversarial setting.
result Bi-GAN model can impute and predict missing values for time-series of varying length.
Auto-regressive models improve smoothing efficiency with exponentially tapered windows.
problem Improving time-series smoothing efficiency.
method An auto-regressive formulation for time-series smoothing.
result Auto-regressive models result in moving means with exponentially tapered windows.
Efficient online kernel CUSUM detects changes quickly and accurately.
problem Detecting changes in online data streams efficiently.
method Online kernel CUSUM using maximum kernel statistics.
result Increased sensitivity to small changes compared to existing methods.
Recurrent auto-encoder model summarises sequential data through an encoder structure into a fixed-length vector and then reconstructs the original sequence through the decoder structure. The summarised vector can be used to represent time series features. In this paper, we propose relaxing the dimensionality of the dec…
lCARE improves EVaR model for time-varying tail risk by localizing parameters.
problem Time-varying tail risk in financial portfolios.
method Local parametric approach to fit expectile models, optimizing interval length.
result Optimal interval lengths for tail risk capture (3-6 months) improve risk assessment.
Paper uses topological data analysis for time series classification.
problem Classifying univariate time series data, especially physiological signals.
method Persistent homology for feature engineering, followed by machine learning.
result Higher accuracy achieved with fewer features compared to traditional methods.
TAKDE optimizes kernel density estimation for real-time dynamic processes.
problem Real-time density estimation in applications like computer vision and signal processing.
method Derives asymptotic mean integrated squared error (AMISE) upper bound for 'sliding window' kernel density estimator and proposes TAKDE as a novel, theoretically optimal estimator.
result TAKDE outperforms other dynamic density estimators in terms of test log-likelihood and runtime.
Develops a new trading strategy for renewable producers to manage price volatility.
problem Price volatility and imbalance risk in power markets due to renewable generation.
method Data-driven continuous-time stochastic optimal control framework using SDEs and diffusion models.
result Trading strategy outperforms benchmarks and reduces profit and loss.
The paper analyzes the sliding regret of stochastic bandit algorithms.
problem Measuring the one-shot behavior of no-regret algorithms in stochastic bandits.
method Introducing sliding regret to measure the worst pseudo-regret over a time-window.
result Randomized methods have optimal sliding regret, while index policies have the worst possible sliding regret.
The study optimizes investment portfolios using deep learning models for variance-covariance estimation.
problem Estimating an appropriate variance-covariance matrix in Modern Portfolio Theory.
method Employed LSTM-RNN and probabilistic deep learning models (DeepVAR, GPVAR) for multivariate forecasting and portfolio optimization.
result LSTM-RNN models generally yield the best performance in terms of information ratio and annualized returns.
Improved algorithm for optimal stopping problems reduces runtime.
problem Optimal stopping problems with infinite time horizon and random discounting.
method Flexible forward improvement iteration with a variable look-ahead distance.
result The new algorithm converges and can significantly reduce runtime.
XGBoost predicts NEPSE Index log returns with low error and high directional accuracy.
problem Forecasting daily log-returns in the NEPSE Index with high accuracy.
method XGBoost machine learning, feature engineering, hyperparameter optimization, walk-forward validation.
result Optimal XGBoost configuration achieves lowest log-return RMSE and MAE.
Driver identification has emerged as a vital research field, where both practitioners and researchers investigate the potential of driver identification to enable a personalized driving experience. Within recent years, a selection of studies have reported that individuals could be perfectly identified based on their dr…
New algorithm for nonstationary multi-armed bandits with optimal performance.
problem Nonstationary multi-armed bandits with changing model parameters over time.
method Adaptive Resetting Bandit (ADR-bandit) algorithm using adaptive windowing techniques.
result ADR-bandit achieves nearly optimal performance in both abrupt and gradual changes.
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…
Unified formulation bridges adversarial and nonstationary bandits.
problem Handling time-varying reward distributions in multi-armed bandit problems.
method Unified oracle that switches between adversarial and nonstationary bandit oracles based on window size.
result Optimal regret achieved with matching lower bound.
We consider a model of selective prediction, where the prediction algorithm is given a data sequence in an online fashion and asked to predict a pre-specified statistic of the upcoming data points. The algorithm is allowed to choose when to make the prediction as well as the length of the prediction window, possibly de…
Quantum kernel improves solar irradiance forecasting.
problem Improving short-term solar irradiance forecasting accuracy.
method Quantum Fourier Transform kernel in KRR with feature mixing.
result Consistently improves R2 and nRMSE over classical kernels.