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

Showing 19011925 of 6748 papers

TitleStatusHype
A Capsule Network for Traffic Speed Prediction in Complex Road NetworksCode0
Block-Structure Based Time-Series Models For Graph SequencesCode0
Defending Black-box Skeleton-based Human Activity ClassifiersCode0
A Wavelet Method for Panel Models with Jump Discontinuities in the ParametersCode0
Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive LearningCode0
A Wild Bootstrap for Degenerate Kernel TestsCode0
Block Hankel Tensor ARIMA for Multiple Short Time Series ForecastingCode0
Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural NetworksCode0
Distributed and parallel time series feature extraction for industrial big data applicationsCode0
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing FlowsCode0
BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge DistillationCode0
Black box variational inference for state space modelsCode0
A General Deep Learning Framework for Network Reconstruction and Dynamics LearningCode0
Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell CultivationCode0
Discrete signature and its application to financeCode0
Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly DetectionCode0
Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time SeriesCode0
Discovering physical concepts with neural networksCode0
Discovering Synchronized Subsets of Sequences: A Large Scale SolutionCode0
Discovering long term dependencies in noisy time series data using deep learningCode0
Discovering patterns of online popularity from time seriesCode0
DisCoVQA: Temporal Distortion-Content Transformers for Video Quality AssessmentCode0
Dilated Convolutional Neural Networks for Time Series ForecastingCode0
Bioluminescence modeling for deep sea experimentsCode0
Action Recognition Using Volumetric Motion RepresentationsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified