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 3851–3875 of 6748 papers

TitleStatusHype
Superiority of Simplicity: A Lightweight Model for Network Device Workload PredictionCode1
Multivariate Time Series Classification Using Spiking Neural Networks—0
Classification with 2-D Convolutional Neural Networks for breast cancer diagnosisCode0
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural NetworksCode0
Leveraging Class Hierarchies with Metric-Guided Prototype LearningCode1
Compact representation of temporal processes in echosounder time series via matrix decomposition—0
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approachCode1
Inference on the change point in high dimensional time series models via plug in least squares—0
High-recall causal discovery for autocorrelated time series with latent confounders—0
Dalek -- a deep-learning emulator for TARDIS—0
Path Signatures on Lie GroupsCode0
Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential EquationsCode0
Lightweight Temporal Self-Attention for Classifying Satellite Image Time SeriesCode1
Handling Variable-Dimensional Time Series with Graph Neural Networks—0
Subject-Aware Contrastive Learning for BiosignalsCode1
Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation—0
Conditional GAN for timeseries generationCode1
Semi-supervised Sequential Generative Models—0
Cyclical Electromechanical Error Denial System Using Matrix Profile—0
Coloured noise time series as appropriate models for environmental variation in artificial evolutionary systems—0
Neural Time Warping For Multiple Sequence Alignment—0
Development of an Algorithm for Identifying Changes in System Dynamics from Time Series—0
Risk Management and Return Prediction—0
Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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