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 6126–6150 of 6748 papers

TitleStatusHype
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models—0
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets—0
Earliness-Aware Deep Convolutional Networks for Early Time Series Classification—0
Low Latency Anomaly Detection and Bayesian Network Prediction of Anomaly Likelihood—0
Measurement of Anticipative Power of a Retina by Predictive Information—0
Why is it Difficult to Detect Sudden and Unexpected Epidemic Outbreaks in Twitter?—0
Combining observational and experimental data to find heterogeneous treatment effects—0
NonSTOP: A NonSTationary Online Prediction Method for Time Series—0
Learning Time Series Detection Models from Temporally Imprecise Labels—0
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data ProcessingCode1
Causal Compression—0
The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks—0
All-atom Molecular Dynamics Simulations of the Projection Domain of the Intrinsically Disordered htau40 Protein—0
Limits to causal inference with state-space reconstruction for infectious disease—0
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories—0
Tool and Phase recognition using contextual CNN features—0
Recurrent switching linear dynamical systemsCode0
Universality of Bayesian mixture predictors—0
Are Chinese transport policies effective? A new perspective from direct pollution rebound effect, and empirical evidence from road transport sector—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Distributed and parallel time series feature extraction for industrial big data applicationsCode0
SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time SeriesCode0
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition—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