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.
Paper introduces a differentiable STFT for continuous window length optimization.
Study predicts cryptocurrency trends using LSTM model.
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 with a moving average window length . We analytically show that the coefficient…
This study optimizes trading strategy parameters using walk-forward techniques and finds robust performance.
Improved driver identification accuracy using steering wheel data.
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…
Adaptive beamforming collapses in highly non-stationary environments, but the Universal Switching Beamformer resolves this by dynamically adjusting memory length.
LASSO-PCA combines LASSO and PCA for automated forecast averaging.
lCARE improves EVaR model for time-varying tail risk by localizing parameters.
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.
ALT improves TSC by capturing complex patterns in time series data.
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…
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…
New algorithm optimizes resource allocation in non-stationary networks.
SummerTime summarizes variable-length time series for machine learning applications.
The paper tackles long-context linear system identification with improved sample complexity bounds.
Shorter time windows and carefully selected features outperform longer periods and extra features in mortgage default prediction.
Study improves portfolio optimization for Indonesian banks using robust methods.
Pairs trading strategy improved using Ornstein-Uhlenbeck process.
Modeling cryptocurrency volatility and jumps with SVCJ model.
Spike sorting is a fundamental preprocessing step in neuroscience that is central to access simultaneous but distinct neuronal activities and therefore to better understand the animal or even human brain. But numerical complexity limits studies that require processing large scale datasets in terms of number of electrod…
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…
Graph Pointer Networks and hierarchical reinforcement learning solve combinatorial optimization problems like TSP.
Bi-GAN model for imputing and predicting irregular time-series data.
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…
Long-range correlation and fluctuation in the gold market time series of world's two leading gold consuming countries, namely China and India, are studied. For both the market series during the period 1985-2013 we observe a long-range persistence of memory in the sequences of maxima (minima) of returns in successive ti…
Paper uses topological data analysis for time series classification.
Improved algorithm reduces excess risk in selective learning.
The paper studies knot densities under various constraints and degenerations.
Optimal weight windows are symmetric rectangles centered at peak.
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 …
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…
Improved algorithm for optimal stopping problems reduces runtime.
Apparently random financial fluctuations often exhibit varying levels of complexity, chaos. Given limited data, predictability of such time series becomes hard to infer. While efficient methods of Lyapunov exponent computation are devised, knowledge about the process driving the dynamics greatly facilitates the complex…
This paper analyzes DeepWalk and node2vec for community detection in large networks.
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.
Study improves queue length estimation from connected vehicles by filtering parameters.
New approach reduces malware detection memory requirements and speeds up training.
We prove that for every analytic curve in the complex plane, Euclidean and spherical arc-lengths are global conformal parameters. We also prove that for any analytic curve in the hyperbolic plane, hyperbolic arc-length is also a global parameter. We generalize some of these results to the case of analytic curves in Euc…
Robust algorithm detects season length without parameters.
Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits dynamic changes. The e…
Transformer model predicts stock trends using technical data and sentiment analysis.
Differentiable Window improves attention modules by enabling more focused attentions.
We investigate quotation and transaction activities in the foreign exchange market for every week during the period of June 2007 to December 2010. A scaling relationship between the mean values of number of quotations (or number of transactions) for various currency pairs and the corresponding standard deviations holds…
SmoothFBO tackles non-stationary functional bilevel optimization.
In this paper, we present an end-to-end approach for environmental sound classification based on a 1D Convolution Neural Network (CNN) that learns a representation directly from the audio signal. Several convolutional layers are used to capture the signal's fine time structure and learn diverse filters that are relevan…