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 4601–4625 of 6748 papers

TitleStatusHype
GesturePod: Enabling On-device Gesture-based Interaction for White Cane Users—0
Coupling Oceanic Observation Systems to Study Mesoscale Ocean DynamicsCode0
Temporal Network Sampling—0
Forecasting under Long Memory and Nonstationarity—0
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models—0
Variationally Inferred Sampling Through a Refined BoundCode0
Scalable Gradients and Variational Inference for Stochastic Differential Equations—0
Automated Detection of Left Ventricle in Arterial Input Function Images for Inline Perfusion Mapping using Deep Learning: A study of 15,000 Patients—0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness—0
Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI—0
A Single Scalable LSTM Model for Short-Term Forecasting of Disaggregated Electricity Loads—0
Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural NetworkCode0
Adaptive Transfer Learning of Multi-View Time Series Classification—0
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainabilityCode0
Bayesian Temporal Factorization for Multidimensional Time Series PredictionCode2
Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems—0
Decoding Working Memory Load from EEG with LSTM Networks—0
Accelerometer-Based Gait Segmentation: Simultaneously User and Adversary Identification—0
Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson's Disease—0
The Expressivity and Training of Deep Neural Networks: toward the Edge of Chaos?—0
Predicting dynamical system evolution with residual neural networks—0
Evolving Gaussian Process kernels from elementary mathematical expressions—0
Phase space reconstruction from a biological time series. A PhotoPlethysmoGraphic signal a case study—0
On Computational Complexity Reduction Methods for Kalman Filter Extensions—0
Time series classification for varying length 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