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 401–425 of 6748 papers

TitleStatusHype
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic ForecastingCode1
Feature-Based Time-Series Analysis in R using the theft PackageCode1
FedMood: Federated Learning on Mobile Health Data for Mood DetectionCode1
Federated Foundation Models on Heterogeneous Time SeriesCode1
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data DetectionCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series ForecastingCode1
Financial Time Series Data Processing for Machine LearningCode1
Finding active galactic nuclei through FinkCode1
Finding Scientific Topics in Continuously Growing Text CorporaCode1
Accelerating Recurrent Neural Networks for Gravitational Wave ExperimentsCode1
First De-Trend then Attend: Rethinking Attention for Time-Series ForecastingCode1
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel SizesCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
An Empirical Survey of Data Augmentation for Time Series Classification with Neural NetworksCode1
Forecasting with sktime: Designing sktime's New Forecasting API and Applying It to Replicate and Extend the M4 StudyCode1
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series ForecastingCode1
Adaptive Conformal Predictions for Time SeriesCode1
From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecastingCode1
From Time Series to Networks in R with the ts2net PackageCode1
Adjusting for Autocorrelated Errors in Neural Networks for Time SeriesCode1
Fully Spiking Variational AutoencoderCode1
Gaussian Process Prior Variational AutoencodersCode1
Generalised Interpretable Shapelets for Irregular Time SeriesCode1
Can LLMs Understand Time Series Anomalies?Code1
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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