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 4601–4650 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
Graph Spectral Embedding for Parsimonious Transmission of Multivariate Time Series—0
Probabilistic sequential matrix factorizationCode0
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth MeasureCode0
Spatio-Temporal Alignments: Optimal transport through space and timeCode1
A Joint Model for IT Operation Series Prediction and Anomaly Detection—0
Continual Learning Using Bayesian Neural Networks—0
Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies—0
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula ProcessesCode0
Patterns of Urban Foot Traffic Dynamics—0
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion—0
Unsupervised Representation for EHR Signals and Codes as Patient Status Vector—0
On the Duality between Network Flows and Network Lasso—0
DPSOM: Deep Probabilistic Clustering with Self-Organizing MapsCode0
Stationarity of the detrended price return in stock markets—0
The Neural Moving Average Model for Scalable Variational Inference of State Space ModelsCode0
Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels—0
MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive—0
End-to-end learning of energy-based representations for irregularly-sampled signals and imagesCode0
A data imputation method for multivariate time series based on generative adversarial network—0
Normalisation of Weights and Firing Rates in Spiking Neural Networks with Spike-Timing-Dependent Plasticity—0
ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning—0
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open QuestionsCode0
A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network—0
Machine Learning vs Statistical Methods for Time Series Forecasting: Size MattersCode0
Stochastic Weight Matrix-based Regularization Methods for Deep Neural NetworksCode0
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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