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 5476–5500 of 6748 papers

TitleStatusHype
Probabilistic structure discovery in time series data—0
Probabilistic Temporal Subspace Clustering—0
Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments—0
Probabilistic Transformer For Time Series Analysis—0
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models—0
Probabilistic water demand forecasting using quantile regression algorithms—0
ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information—0
Process Knowledge Driven Change Point Detection for Automated Calibration of Discrete Event Simulation Models Using Machine Learning—0
Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems—0
Process Monitoring Using Maximum Sequence Divergence—0
Process Outcome Prediction: CNN vs. LSTM (with Attention)—0
Production Function of the Mining Sector of Iran—0
Product Reservoir Computing: Time-Series Computation with Multiplicative Neurons—0
ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles—0
Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective—0
Prognostic classification based on random convolutional kernel—0
Progressive Cross-modal Knowledge Distillation for Human Action Recognition—0
Progressive Fusion for Multimodal Integration—0
Progressive Growing of Neural ODEs—0
Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences—0
Projection assisted Dynamic Mode Decomposition of large scale data—0
Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network—0
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning—0
Proofs and additional experiments on Second order techniques for learning time-series with structural breaks—0
Propagation Graph Estimation from Individual's Time Series of Observed States—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