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 6351–6375 of 6748 papers

TitleStatusHype
Deep Markov Spatio-Temporal FactorizationCode0
Applying Machine Learning to Crowd-sourced Data from Earthquake DetectiveCode0
Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTWCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Complex Gated Recurrent Neural NetworksCode0
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent KernelsCode0
Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series ClassifiersCode0
Time series anomaly detection with reconstruction-based state-space modelsCode0
Semantic Discord: Finding Unusual Local Patterns for Time SeriesCode0
Fast and Robust Online Inference with Stochastic Gradient Descent via Random ScalingCode0
Precision and Recall for Time SeriesCode0
Autonomous Deep Quality Monitoring in Streaming EnvironmentsCode0
Data-driven approach in a compartmental epidemic model to assess undocumented infectionsCode0
Apply Artificial Neural Network to Solving Manpower Scheduling ProblemCode0
Synthesis of Parametric Hybrid Automata from Time SeriesCode0
Fast and Accurate Time Series Classification with WEASELCode0
False Negative Reduction in Video Instance Segmentation using Uncertainty EstimatesCode0
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries ChallengeCode0
Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural NetworksCode0
Variational online learning of neural dynamicsCode0
Memory-Efficient Centroid Decomposition for Long Time SeriesCode0
Fairness in Forecasting of Observations of Linear Dynamical SystemsCode0
Memory-Gated Recurrent NetworksCode0
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal DynamicsCode0
Adaptive Multi-Agent Continuous Learning SystemCode0
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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