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 51–75 of 6748 papers

TitleStatusHype
Model scale versus domain knowledge in statistical forecasting of chaotic systemsCode2
LogAI: A Library for Log Analytics and IntelligenceCode2
Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and ChallengesCode2
Anomaly Transformer: Time Series Anomaly Detection with Association DiscrepancyCode2
Liquid Structural State-Space ModelsCode2
AR-Net: A simple Auto-Regressive Neural Network for time-seriesCode2
An Extensive Data Processing Pipeline for MIMIC-IVCode2
Liquid Time-constant NetworksCode2
Non-stationary Transformers: Exploring the Stationarity in Time Series ForecastingCode2
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing FlowsCode2
Deep Learning for Time Series Forecasting: Tutorial and Literature SurveyCode2
NeuralProphet: Explainable Forecasting at ScaleCode2
Harnessing Vision Models for Time Series Analysis: A SurveyCode2
Generative Time Series Forecasting with Diffusion, Denoise, and DisentanglementCode2
HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in PythonCode2
FITS: Modeling Time Series with 10k ParametersCode2
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic ForecastingCode2
Flowformer: Linearizing Transformers with Conservation FlowsCode2
ETSformer: Exponential Smoothing Transformers for Time-series ForecastingCode2
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series ForecastingCode2
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series ForecastingCode2
How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and OutlookCode2
Diffusion-based Time Series Imputation and Forecasting with Structured State Space ModelsCode2
Learning Deep Time-index Models for Time Series ForecastingCode2
Domino: Discovering Systematic Errors with Cross-Modal EmbeddingsCode2
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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