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 1201–1225 of 6748 papers

TitleStatusHype
Latent Space Unsupervised Semantic Segmentation—0
Multi-temporal speckle reduction with self-supervised deep neural networks—0
Time-Varying Poisson Autoregression—0
POP: Mining POtential Performance of new fashion products via webly cross-modal query expansionCode0
Solving the optimal stopping problem with reinforcement learning: an application in financial option exerciseCode0
Efficiency of the Moscow Stock Exchange before 2022—0
Exploring Financial Networks Using Quantile Regression and Granger Causality—0
Estimating value at risk: LSTM vs. GARCH—0
MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation—0
A Convolutional Neural Network Approach to Supernova Time-Series Classification—0
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense MechanismsCode5
Using Neural Networks by Modelling Semi-Active Shock Absorber—0
Probabilistic Reconciliation of Count Time Series—0
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers—0
Task-aware Similarity Learning for Event-triggered Time Series—0
Testing for explosive bubbles: a review—0
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting SystemCode0
Transfer learning for time series classification using synthetic data generationCode1
Multiscale Causal Structure Learning—0
Greykite: Deploying Flexible Forecasting at Scale at LinkedInCode3
A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time SeriesCode0
Outlier detection of vital sign trajectories from COVID-19 patientsCode0
StockBot: Using LSTMs to Predict Stock PricesCode0
Spatiotemporal Propagation Learning for Network-Wide Flight Delay PredictionCode1
Rethinking Attention Mechanism in Time Series Classification—0
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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