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 4951–4975 of 6748 papers

TitleStatusHype
Efficient Kernel-based Subsequence Search for User Identification from Walking Activity—0
Extending Deep Learning Models for Limit Order Books to Quantile Regression—0
Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications—0
Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions—0
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend FilteringCode0
A Statistical Recurrent Stochastic Volatility Model for Stock Markets—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-SeriesCode0
ZeLiC and ZeChipC: Time Series Interpolation Methods for Lebesgue or Event-based Sampling—0
Energy Predictive Models with Limited Data using Transfer Learning—0
Evolution of Hierarchical Structure & Reuse in iGEM Synthetic DNA Sequences—0
Application of Machine Learning to accidents detection at directional drilling—0
Mutual Information and the Edge of Chaos in Reservoir Computers—0
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian—0
Neural Learning of Online Consumer Credit Risk—0
CCMI : Classifier based Conditional Mutual Information EstimationCode0
PI-Net: A Deep Learning Approach to Extract Topological Persistence ImagesCode0
Streaming Variational Monte CarloCode0
Manifold-regression to predict from MEG/EEG brain signals without source modelingCode0
Automatic Health Problem Detection from Gait Videos Using Deep Neural NetworksCode0
A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series—0
Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization—0
Cellular Traffic Prediction and Classification: a comparative evaluation of LSTM and ARIMA—0
Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of SnapchatCode0
Context Dependent Semantic Parsing over Temporally Structured Data—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