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 1426–1450 of 6748 papers

TitleStatusHype
BolT: Fused Window Transformers for fMRI Time Series AnalysisCode1
Forecasting of Non-Stationary Sales Time Series Using Deep Learning—0
GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection—0
Time-series Transformer Generative Adversarial NetworksCode3
Deep Direct Discriminative Decoders for High-dimensional Time-series Data AnalysisCode0
A Novel Markov Model for Near-Term Railway Delay Prediction—0
Individual Topology Structure of Eye Movement Trajectories—0
Persistent Homology of Coarse Grained State Space Networks—0
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts—0
The Forecasting performance of the Factor model with Martingale Difference errors—0
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical SystemsCode0
Self-Supervised Time Series Representation Learning via Cross Reconstruction TransformerCode1
Conformal Prediction with Temporal Quantile Adjustments—0
RiskLoc: Localization of Multi-dimensional Root Causes by Weighted Risk—0
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder—0
Neural Additive Models for Nowcasting—0
Classifying Human Activities using Machine Learning and Deep Learning Techniques—0
Time Series Anomaly Detection via Reinforcement Learning-Based Model SelectionCode1
Extract Dynamic Information To Improve Time Series Modeling: a Case Study with Scientific Workflow—0
Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data—0
Parallel bandit architecture based on laser chaos for reinforcement learning—0
Inferring extended summary causal graphs from observational time series—0
Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems—0
Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics—0
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints—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