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

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Showing 6151–6175 of 6748 papers

TitleStatusHype
A Bayesian Ensemble for Unsupervised Anomaly Detection—0
Representation Learning with Deconvolution for Multivariate Time Series Classification and VisualizationCode0
Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time SeriesCode0
Techniques for multifractal spectrum estimation in financial time series—0
Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks—0
Scalable Pooled Time Series of Big Video Data from the Deep Web—0
Maximally Divergent Intervals for Anomaly Detection—0
Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalisation—0
An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes—0
Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model—0
A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade—0
Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity—0
Lensless Imaging with Compressive Ultrafast Sensing—0
Similarity Learning for Time Series Classification—0
Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models—0
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsCode1
Multiple Regularizations Deep Learning for Paddy Growth Stages Classification from LANDSAT-8—0
Combining Generative and Discriminative Neural Networks for Sleep Stages Classification—0
Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates—0
End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks—0
Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data—0
EXTRACT: Strong Examples from Weakly-Labeled Sensor Data—0
Automatic Construction of a Recurrent Neural Network based Classifier for Vehicle Passage Detection—0
Robust Time-Series Retrieval Using Probabilistic Adaptive Segmental Alignment—0
Global Constraint Catalog, Volume II, Time-Series 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