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.
RTFN extracts robust temporal features for time series analysis.
TGAT learns node embeddings for evolving graphs, capturing both static and temporal features.
TIME explains temporal models by analyzing feature importance.
Combines CNN and LSTM for spatio-temporal graph networks.
Enhances SNNs for spatio-temporal feature extraction.
Temporal-difference and Q-learning learn feature representations that converge to optimal ones.
Accurately predicting customer churn using large scale time-series data is a common problem facing many business domains. The creation of model features across various time windows for training and testing can be particularly challenging due to temporal issues common to time-series data. In this paper, we will explore …
TSAM predicts directed temporal links using GCN and self-attention.
STAS selects optimal spatio-temporal scales for bias correction in precipitation forecasts.
Hybrid model improves COVID-19 case forecasting accuracy.
Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. Here we propose a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupe…
StrGNN detects anomalies in dynamic graphs by analyzing subgraphs and temporal features.
HRTPP improves TPP interpretability and accuracy in medical event modeling.
Paper tackles class-incremental time series classification with dual-stream feature extraction.
Wind power prediction is of vital importance in wind power utilization. There have been a lot of researches based on the time series of the wind power or speed, but In fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction. In th…
This paper tackles spatio-temporal information preservation in machine learning.
IETNet identifies important channels for MVTS classification.
New framework for analyzing hydroclimatic time series across multiple scales.
Proposes a feature transformation for spatio-temporal traffic models to improve performance and transferability.
The spatio-temporal graph learning is becoming an increasingly important object of graph study. Many application domains involve highly dynamic graphs where temporal information is crucial, e.g. traffic networks and financial transaction graphs. Despite the constant progress made on learning structured data, there is s…
CODA simulates future data to generalize models across different datasets.
Multivariate time series prediction has applications in a wide variety of domains and is considered to be a very challenging task, especially when the variables have correlations and exhibit complex temporal patterns, such as seasonality and trend. Many existing methods suffer from strong statistical assumptions, numer…
Acoustic scenes are rich and redundant in their content. In this work, we present a spatio-temporal attention pooling layer coupled with a convolutional recurrent neural network to learn from patterns that are discriminative while suppressing those that are irrelevant for acoustic scene classification. The convolutiona…
Unified model learns from both time-series and cross-sectional momentum features.
We propose Power Slow Feature Analysis, a gradient-based method to extract temporally slow features from a high-dimensional input stream that varies on a faster time-scale, as a variant of Slow Feature Analysis (SFA) that allows end-to-end training of arbitrary differentiable architectures and thereby significantly ext…
We propose the application of a high-speed maximum likelihood clustering algorithm to detect temporal financial market states, using correlation matrices estimated from intraday market microstructure features. We first determine the ex-ante intraday temporal cluster configurations to identify market states, and then st…
We present an approach to estimate the severity of traffic related accidents in aggregated (area-level) and disaggregated (point level) data. Exploring spatial features, we measure complexity of road networks using several area level variables. Also using temporal and other situational features from open data for New Y…
Paper analyzes TD() convergence rates for arbitrary features.
We study the problem of classifying interval-based temporal sequences (IBTSs). Since common classification algorithms cannot be directly applied to IBTSs, the main challenge is to define a set of features that effectively represents the data such that classifiers can be applied. Most prior work utilizes frequent patter…
MTRGL learns temporal correlations from multi-modal data for improved pair trading.
Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM)…
Proposes a dynamic model for urban traffic volume prediction.
Whole MILC learns brain disorder dynamics from unlabeled data.
In various web applications like targeted advertising and recommender systems, the available categorical features (e.g., product type) are often of great importance but sparse. As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for …
A novel distance measure aligns time series with feature and temporal variability.
Proposes a new tensor decomposition method for functional temporal data with adaptive complexity.
Community detection has long been an important yet challenging task to analyze complex networks with a focus on detecting topological structures of graph data. Essentially, real-world graph data contains various features, node and edge types which dynamically vary over time, and this invalidates most existing community…
When sensors collect spatio-temporal data in a large geographical area, the existence of missing data cannot be escaped. Missing data negatively impacts the performance of data analysis and machine learning algorithms. In this paper, we study deep autoencoders for missing data imputation in spatio-temporal problems. We…
CoI framework models clinical feature interactions, revealing temporal dependencies and enhancing transparency.
We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) designed for temporal signal recognition. The CLNN takes into consideration the temporal nature of the sound signal and the MCLNN extends upon the CLNN through a binary mask to preserve the spatial locality of the feature…
STNN models forecast COVID-19 spread with improved accuracy.
Study analyzes seasonal hydroclimatic features across climates and continents.
Recent studies have demonstrated the power of recurrent neural networks for machine translation, image captioning and speech recognition. For the task of capturing temporal structure in video, however, there still remain numerous open research questions. Current research suggests using a simple temporal feature pooling…
BrainCast predicts whole-brain fMRI time series from short scans.
MIP framework improves urban flow prediction by adapting to distribution shifts.
Recent works demonstrated the usefulness of temporal coherence to regularize supervised training or to learn invariant features with deep architectures. In particular, enforcing smooth output changes while presenting temporally-closed frames from video sequences, proved to be an effective strategy. In this paper we pro…
Short-term demand forecasting models commonly combine convolutional and recurrent layers to extract complex spatiotemporal patterns in data. Long-term histories are also used to consider periodicity and seasonality patterns as time series data. In this study, we propose an efficient architecture, Temporal-Guided Networ…
Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.