SOTAVerified

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 2751–2800 of 6748 papers

TitleStatusHype
TS-BERT: A fusion model for Pre-trainning Time Series-Text Representations—0
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?—0
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data—0
A Study of Aggregation of Long Time-series Input for LSTM Neural Networks—0
Predicting subscriber usage: Analyzing multi-dimensional time-series using Convolutional Neural Networks—0
Context-invariant, multi-variate time series representations—0
Loss meta-learning for forecasting—0
TRAKR – A reservoir-based tool for fast and accurate classification of neural time-series patterns—0
Multi-Task Processes—0
Mixed-Memory RNNs for Learning Long-term Dependencies in Irregularly Sampled Time Series—0
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting—0
Objective Evaluation of Deep Visual Interpretations on Time Series Data—0
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis—0
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series—0
Coherence-based Label Propagation over Time Series for Accelerated Active Learning—0
Sample Complexity of the Robust LQG Regulator with Coprime Factors Uncertainty—0
ACCTS: an Adaptive Model Training Policy for Continuous Classification of Time Series—0
Kernel distance measures for time series, random fields and other structured data—0
Cluster-based Feature Importance Learning for Electronic Health Record Time-series—0
LatTe Flows: Latent Temporal Flows for Multivariate Sequence Analysis—0
Provably Calibrated Regression Under Distribution Drift—0
Modelling neuronal behaviour with time series regression: Recurrent Neural Networks on synthetic C. elegans data—0
Integrating Attention Feedback into the Recurrent Neural NetworkCode0
Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks—0
MECATS: Mixture-of-Experts for Probabilistic Forecasts of Aggregated Time Series—0
CDPS: Constrained DTW-Preserving Shapelets—0
Variational Inference for Continuous-Time Switching Dynamical Systems—0
Causal Triple Attention Time Series Forecasting—0
Information-Aware Time Series Meta-Contrastive Learning—0
Adaptive Bayesian Sum of Trees Model for Covariate Dependent Spectral Analysis—0
Huber Additive Models for Non-stationary Time Series Analysis—0
An Optimally Weighted Echo State Neural Network for Highly Chaotic Time Series Modelling—0
Taking ROCKET on an efficiency mission: A distributed solution for fast and accurate multivariate time series classification—0
SCformer: Segment Correlation Transformer for Long Sequence Time Series Forecasting—0
Less is more: Selecting the right benchmarking set of data for time series classification—0
BLUnet: Arithmetic-free Inference with Bit-serialised Table Lookup Operation for Efficient Deep Neural Networks—0
Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs—0
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification—0
Improving Time Series Classification Algorithms Using Octave-Convolutional Layers—0
An Empirical Analysis of how Internet Access Influences Public Opinion towards Undocumented Immigrants and Unaccompanied ChildrenCode0
Data-driven Residual Generation for Early Fault Detection with Limited Data—0
The VVAD-LRS3 Dataset for Visual Voice Activity Detection—0
Lithium-ion Battery State of Health Estimation based on Cycle Synchronization using Dynamic Time Warping—0
Macroeconomic forecasting with LSTM and mixed frequency time series data—0
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle—0
Time Series Model Attribution Visualizations as Explanations—0
Empirics on the expressiveness of Randomized Signature—0
Improving the Thermal Infrared Monitoring of Volcanoes: A Deep Learning Approach for Intermittent Image Series—0
System Identification in Multi-Actuator Hard Disk Drives with Colored Noises using Observer/Kalman Filter Identification (OKID) Framework—0
Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning—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