AR-Sieve Bootstrap for Random Forest: A Simulation-Based Comparison with Rangerts for Time Series Prediction
The AR-Sieve Bootstrap (ARSB) can create more diverse trees in Random Forest, leading to improved forecasting accuracy compared to other bootstrap strategies, especially for time series with a dominant autoregressive component.
Coherent Forecasting of the Novel Geometric Autoregressive (NoGeAR(1)) Model
The core message of this article is to propose a coherent forecasting methodology for integer-valued time series data using the recently introduced NoGeAR(1) model, and to demonstrate its efficacy through simulation studies and real-data applications.
Comprehensive Visualization of Traffic Congestion Patterns in Chicago Using Time Series Data
Effective visualization techniques can provide valuable insights into time series data, enabling better understanding of trends, patterns, and relationships between variables.
딥러닝 기반 추세를 가진 VAR 모델: DeepVARwT
시계열 데이터의 추세와 의존 구조를 동시에 모델링하는 새로운 딥러닝 기반 접근법을 제시한다.
Deep Learning for Modeling and Forecasting Multivariate Time Series with Trends
The authors propose a deep learning-based approach, called DeepVARwT, for modeling and forecasting multivariate time series with trends. The method simultaneously estimates the trend and the dependence structure using a Long Short-Term Memory (LSTM) network and a vector autoregressive (VAR) model.
Detecting Long-Timescale Pairwise Interactions Between Time Series Using Feature-Based Information Theory
A feature-based information-theoretic approach can outperform traditional signal-based methods in detecting long-timescale pairwise interactions between time series, especially in scenarios with short time-series lengths, high noise levels, and long interaction timescales.
CARLA: A Self-supervised Contrastive Representation Learning Approach for Effective Time Series Anomaly Detection
CARLA is a novel two-stage self-supervised contrastive representation learning approach that effectively detects anomalies in time series data by learning discriminative representations that distinguish normal and anomalous patterns.
TimeCSL: An Unsupervised Contrastive Learning Framework for Interpretable Time Series Analysis
TimeCSL is an end-to-end system that leverages unsupervised contrastive learning of general shapelets to enable flexible and interpretable time series analysis across various tasks such as classification, clustering, and anomaly detection.
Extracting Frequent Shapes in Time Series under User-Level Local Differential Privacy
The core message of this paper is to propose PrivShape, a trie-based mechanism under user-level local differential privacy (LDP) to effectively extract frequent shapes from time series data while preserving privacy.
End-to-End Self-tuning Self-supervised Framework for Versatile Time Series Anomaly Detection
TSAP, a novel self-tuning self-supervised framework, can automatically select the appropriate anomaly type and tune the associated continuous hyperparameters to effectively detect diverse time series anomalies without any labeled data.
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