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 5276–5300 of 6748 papers

TitleStatusHype
Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification—0
Seasonal Stochastic Volatility and the Samuelson Effect in Agricultural Futures Markets—0
Multivariate Forecasting of Crude Oil Spot Prices using Neural Networks—0
Entropy and Transfer Entropy: The Dow Jones and the build up to the 1997 Asian Crisis—0
Black-Box Autoregressive Density Estimation for State-Space Models—0
Coupled Recurrent Models for Polyphonic Music Composition—0
Fading of collective attention shapes the evolution of linguistic variants—0
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series DataCode0
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular SamplingCode0
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers—0
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal DynamicsCode0
Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition—0
Transform-Based Multilinear Dynamical System for Tensor Time Series Analysis—0
Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior—0
Short-Term Wind-Speed Forecasting Using Kernel Spectral Hidden Markov Models—0
Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting—0
Real-time Power System State Estimation and Forecasting via Deep Neural NetworksCode0
Structural Damage Detection and Localization with Unknown Post-Damage Feature Distribution Using Sequential Change-Point Detection Method—0
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection—0
Fast Distribution Grid Line Outage Identification with μPMU—0
Adversarial Unsupervised Representation Learning for Activity Time-Series—0
Reduced-order modeling with artificial neurons for gravitational-wave inferenceCode0
Assessing biological models using topological data analysis—0
The Altes Family of Log-Periodic Chirplets and the Hyperbolic Chirplet Transform—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