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 6201–6225 of 6748 papers

TitleStatusHype
"Butterfly Effect" vs Chaos in Energy Futures Markets—0
Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder—0
A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves—0
Conformalized density- and distance-based anomaly detection in time-series data—0
Identifying Seizure Onset Zone from the Causal Connectivity Inferred Using Directed Information—0
Training Echo State Networks with Regularization through Dimensionality Reduction—0
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC—0
Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM—0
Temporal Registration in In-Utero Volumetric MRI Time Series—0
Dynamic structure of stock communities: A comparative study between stock returns and turnover rates—0
Dynamic Principal Component Analysis: Identifying the Relationship between Multiple Air Pollutants—0
Revisiting Causality Inference in Memory-less Transition Networks—0
Signs in time: Encoding human motion as a temporal image—0
A Distance for HMMs based on Aggregated Wasserstein Metric and State RegistrationCode0
Analyzing Linear Dynamical Systems: From Modeling to Coding and LearningCode0
Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms—0
Fast and Simple Optimization for Poisson Likelihood Models—0
Size-Consistent Statistics for Anomaly Detection in Dynamic Networks—0
Towards Building a Political Protest Database to Explain Changes in the Welfare State—0
Mining linguistic tone patterns with symbolic representation—0
Towards a text analysis system for political debates—0
Exploring Long-Term Temporal Trends in the Use of Multiword Expressions—0
Text authorship identified using the dynamics of word co-occurrence networks—0
Dynamic Probabilistic Network Based Human Action Recognition—0
Wavelet algorithm for the identification of P300 ERP component—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