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

Showing 60016050 of 6748 papers

TitleStatusHype
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of IntelligenceCode0
VNIbCReg: VICReg with Neighboring-Invariance and better-Covariance Evaluated on Non-stationary Seismic Signal Time SeriesCode0
The Many-to-Many Mapping Between the Concordance Correlation Coefficient and the Mean Square ErrorCode0
Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image DenoisingCode0
Latent Variable Time-varying Network InferenceCode0
On Mini-Batch Training with Varying Length Time SeriesCode0
RIPPLE: Concept-Based Interpretation for Raw Time Series Models in EducationCode0
Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasksCode0
On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM SignalsCode0
Graph Gamma Process Generalized Linear Dynamical SystemsCode0
Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case StudyCode0
On Periodicity Detection and Structural Periodic SimilarityCode0
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical SystemsCode0
Learnable Dynamic Temporal Pooling for Time Series ClassificationCode0
Learnable Group Transform For Time-SeriesCode0
Learnable Path in Neural Controlled Differential EquationsCode0
Topological Machine Learning for Multivariate Time SeriesCode0
On projection methods for functional time series forecastingCode0
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
TSViz: Demystification of Deep Learning Models for Time-Series AnalysisCode0
RNN-based counterfactual prediction, with an application to homestead policy and public schoolingCode0
Time-Series Event Prediction with Evolutionary State GraphCode0
Correlated daily time series and forecasting in the M4 competitionCode0
Adversarial Generation of Time-Frequency Features with application in audio synthesisCode0
Learning CHARME models with neural networksCode0
Detecting structural perturbations from time series with deep learningCode0
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical FeaturesCode0
Benchmark of Deep Learning Models on Large Healthcare MIMIC DatasetsCode0
Learning compressed representations of blood samples time series with missing dataCode0
Unsupervised Learning for Computational PhenotypingCode0
Multimodal Transformer for Unaligned Multimodal Language SequencesCode0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
A 1d convolutional network for leaf and time series classificationCode0
Learning Deep Input-Output Stable DynamicsCode0
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product NetworksCode0
Benchmarking time series classification -- Functional data vs machine learning approachesCode0
Graph Edit NetworksCode0
Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural NetworksCode0
Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural NetworkCode0
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural NetworkCode0
Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time SeriesCode0
Twitter conversations predict the daily confirmed COVID-19 casesCode0
On the Metrics and Adaptation Methods for Domain Divergences of sEMG-based Gesture RecognitionCode0
Learning Efficient Representations of Mouse Movements to Predict User AttentionCode0
The UCR Time Series ArchiveCode0
Granger Causality using Neural NetworksCode0
Learning filter widths of spectral decompositions with waveletsCode0
Step Counting with Attention-based LSTMCode0
Gradient-free training of recurrent neural networksCode0
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly DetectionCode0
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Benchmark Results

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