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

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Showing 5301–5325 of 6748 papers

TitleStatusHype
Parsimonious Network based on Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis—0
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning—0
Partial Mobilization: Tracking Multilingual Information Flows Amongst Russian Media Outlets and Telegram—0
Partial Sum Processes of Residual-Based and Wald-type Break-Point Statistics in Time Series Regression Models—0
Particle swarm optimization for time series motif discovery—0
Particle Swarm Optimization of Information-Content Weighting of Symbolic Aggregate Approximation—0
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph—0
PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification—0
Path sampling of recurrent neural networks by incorporating known physics—0
Path Signatures for Seizure Forecasting—0
Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task—0
Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting—0
Pattern-Based Prediction of Population Outbreaks—0
Pattern Discovery in Time Series with Byte Pair Encoding—0
Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space—0
Pattern recognition in micro-trading behaviors before stock price jumps: A framework based on multivariate time series analysis—0
Pattern Recognition in Vital Signs Using Spectrograms—0
Pattern Sampling for Shapelet-based Time Series Classification—0
Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study—0
Patterns of Urban Foot Traffic Dynamics—0
PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction—0
Peak Detection On Data Independent Acquisition Mass Spectrometry Data With Semisupervised Convolutional Transformers—0
PECAIQR: A Model for Infectious Disease Applied to the Covid-19 Epidemic—0
Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data—0
Penalized Quasi-likelihood Estimation and Model Selection in Time Series Models with Parameters on the Boundary—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