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 11511200 of 6748 papers

TitleStatusHype
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk PredictionCode0
Hidden Parameter Recurrent State Space Models For Changing Dynamics ScenariosCode0
Hierarchical Probabilistic Model for Blind Source Separation via Legendre TransformationCode0
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time seriesCode0
Harnessing the power of Topological Data Analysis to detect change points in time seriesCode0
HigeNet: A Highly Efficient Modeling for Long Sequence Time Series Prediction in AIOpsCode0
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge AggregationCode0
Guidelines for Augmentation Selection in Contrastive Learning for Time Series ClassificationCode0
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical SystemsCode0
Graph Edit NetworksCode0
A deep learning architecture to detect events in EEG signals during sleepCode0
Graph Gamma Process Generalized Linear Dynamical SystemsCode0
GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsCode0
Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 PandemicCode0
Gradient-free training of recurrent neural networksCode0
Granger Causality using Neural NetworksCode0
Gesture Recognition in RGB Videos UsingHuman Body Keypoints and Dynamic Time WarpingCode0
A Deep Learning Approach to Probabilistic Forecasting of WeatherCode0
Global Models for Time Series Forecasting: A Simulation StudyCode0
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series DataCode0
Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural NetworkCode0
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal frameworkCode0
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
Generating Reliable Process Event Streams and Time Series Data based on Neural NetworksCode0
Generating Sparse Counterfactual Explanations For Multivariate Time SeriesCode0
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open QuestionsCode0
Generative Adversarial Network for Future Hand Segmentation from Egocentric VideoCode0
Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time SeriesCode0
A Statistical Investigation of Long Memory in Language and MusicCode0
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting SystemCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
AdaVol: An Adaptive Recursive Volatility Prediction MethodCode0
Gated Res2Net for Multivariate Time Series AnalysisCode0
GENDIS: GENetic DIscovery of ShapeletsCode0
Asset Price Forecasting using Recurrent Neural NetworksCode0
Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Longitudinal DataCode0
Fully Neural Network based Model for General Temporal Point ProcessesCode0
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart HomesCode0
GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split AttentionCode0
General anesthesia reduces complexity and temporal asymmetry of the informational structures derived from neural recordings in DrosophilaCode0
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence FunctionsCode0
An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial dataCode0
Sequence Prediction using Spectral RNNsCode0
Assessing Differentially Private Variational Autoencoders under Membership InferenceCode0
Forecasting with Multiple SeasonalityCode0
A CNN adapted to time series for the classification of SupernovaeCode0
Forecasting the Leading Indicator of a Recession: The 10-Year minus 3-Month Treasury Yield SpreadCode0
Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of SnapchatCode0
Forecasting Time Series With Complex Seasonal Patterns Using Exponential SmoothingCode0
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
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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