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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5876–5900 of 6748 papers

TitleStatusHype
A Joint-Entropy Approach To Time-series ClassificationCode0
Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral EnhancementCode0
Reinforcement Learning for Portfolio ManagementCode0
Nowcasting NetworksCode0
Work-in-progress: a deep learning strategy for I/O scheduling in storage systemsCode0
Spectral learning of Bernoulli linear dynamical systems modelsCode0
Spectral Processing of COVID-19 Time-Series DataCode0
Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal DynamicsCode0
Discrete signature and its application to financeCode0
nTreeClus: a Tree-based Sequence Encoder for Clustering Categorical SeriesCode0
I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and EmbeddingCode0
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriesCode0
ISLAND: Interpolating Land Surface Temperature using land coverCode0
DisCoVQA: Temporal Distortion-Content Transformers for Video Quality AssessmentCode0
Cross-Camera Human Motion Transfer by Time Series AnalysisCode0
Biologically-Motivated Deep Learning Method using Hierarchical Competitive LearningCode0
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-SeriesCode0
Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationshipsCode0
Binary Spatial Random Field Reconstruction from Non-Gaussian Inhomogeneous Time-series ObservationsCode0
CRAD: Clustering with Robust Autocuts and DepthCode0
Observational and Interventional Causal Learning for Regret-Minimizing ControlCode0
A Critical Review of Recurrent Neural Networks for Sequence LearningCode0
AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous SensorsCode0
Hierarchical Attention-Based Recurrent Highway Networks for Time Series PredictionCode0
Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV FeedbackCode0
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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