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Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

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Showing 3751–3800 of 6748 papers

TitleStatusHype
Robust language-based mental health assessments in time and space through social media—0
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data—0
Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model—0
Robust M-Estimation for Additive Single-Index Cointegrating Time Series Models—0
Robust Multimodal Fusion for Human Activity Recognition—0
Robust multivariate and functional archetypal analysis with application to financial time series analysis—0
Robustness of sentence length measures in written texts—0
Robust Online Time Series Prediction with Recurrent Neural Networks—0
Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics—0
Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series—0
Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series—0
Robust Structured Low-Rank Approximation on the Grassmannian—0
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks—0
Robust Time Series Chain Discovery with Incremental Nearest Neighbors—0
Robust Time Series Denoising with Learnable Wavelet Packet Transform—0
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection—0
Robust Time-Series Retrieval Using Probabilistic Adaptive Segmental Alignment—0
Robust Trajectory-based Density Estimation for Geometric Structure Recovery: Theory and Applications—0
Cryptocurrency Dynamics: Rodeo or Ascot?—0
Role of assortativity in predicting burst synchronization using echo state network—0
Role of Deep LSTM Neural Networks And WiFi Networks in Support of Occupancy Prediction in Smart Buildings—0
Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings—0
Root-cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systems—0
Rotated spectral principal component analysis (rsPCA) for identifying dynamical modes of variability in climate systems—0
RotLSTM: Rotating Memories in Recurrent Neural Networks—0
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks—0
Rotor Localization and Phase Mapping of Cardiac Excitation Waves using Deep Neural Networks—0
Rough volatility: fact or artefact?—0
Rough volatility of Bitcoin—0
Routine Modeling with Time Series Metric Learning—0
RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Series—0
RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter—0
RTFN: A Robust Temporal Feature Network for Time Series Classification—0
RTFN: Robust Temporal Feature Network—0
Russia-Ukraine war: Modeling and Clustering the Sentiments Trends of Various Countries—0
RV-FuseNet: Range View Based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting—0
S^3NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks—0
S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation—0
SAFCAR: Structured Attention Fusion for Compositional Action Recognition—0
Safe-FinRL: A Low Bias and Variance Deep Reinforcement Learning Implementation for High-Freq Stock Trading—0
SAFE: Spectral Evolution Analysis Feature Extraction for Non-Stationary Time Series Prediction—0
SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series—0
Sales forecasting using WaveNet within the framework of the Kaggle competition—0
Sales Time Series Analytics Using Deep Q-Learning—0
SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise—0
Sample Complexity for Evaluating the Robust Linear Observers Performance under Coprime Factors Uncertainty—0
Sample Complexity of the Robust LQG Regulator with Coprime Factors Uncertainty—0
Sampling Theory of Bandlimited Continuous-Time Graph Signals—0
SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning—0
SARS-CoV-2 Dissemination using a Network of the United States Counties—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified