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 2126–2150 of 6748 papers

TitleStatusHype
Adaptive Sampling for Probabilistic Forecasting under Distribution Shift—0
Elucidation of time-dependent systems biology cell response patterns with time course network enrichment—0
Collaborative adversary nodes learning on the logs of IoT devices in an IoT network—0
Cointegration with Occasionally Binding Constraints—0
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations—0
Cointegration of SARS-CoV-2 Transmission with Weather Conditions and Mobility during the First Year of the COVID-19 Pandemic in the United States—0
Coincident Learning for Unsupervised Anomaly Detection—0
A machine learning approach for forecasting hierarchical time series—0
Efficient Time Series Processing for Transformers and State-Space Models through Token Merging—0
COHORTNEY: Non-Parametric Clustering of Event Sequences—0
Coherent probabilistic forecasts for hierarchical time series—0
A probabilistic graphical model approach in 30 m land cover mapping with multiple data sources—0
Coherence-based Label Propagation over Time Series for Accelerated Active Learning—0
Cognitive state classification using transformed fMRI data—0
Alternating direction method of multipliers for penalized zero-variance discriminant analysis—0
Accuracy of neural networks for the simulation of chaotic dynamics: precision of training data vs precision of the algorithm—0
Efficient Variational Bayes Learning of Graphical Models with Smooth Structural Changes—0
Cognitive forces shape the dynamics of word usage across multiple languages—0
Cognitive Computing to Optimize IT Services—0
A Predictive Online Transient Stability Assessment with Hierarchical Generative Adversarial Networks—0
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification—0
Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model—0
A Predictive Autoscaler for Elastic Batch Jobs—0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series—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