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 2551–2575 of 6748 papers

TitleStatusHype
Data Augmentation techniques in time series domain: A survey and taxonomy—0
A Time-Frequency based Suspicious Activity Detection for Anti-Money Laundering—0
Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting—0
A Deep Structural Model for Analyzing Correlated Multivariate Time Series—0
Data Augmentation for Multivariate Time Series Classification: An Experimental Study—0
Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform—0
DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing—0
A textual transform of multivariate time-series for prognostics—0
Darts: User-Friendly Modern Machine Learning for Time Series—0
DANTE: A framework for mining and monitoring darknet traffic—0
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives—0
DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift—0
DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting—0
A Temporal Fusion Transformer for Long-term Explainable Prediction of Emergency Department Overcrowding—0
Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters—0
A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models—0
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity—0
CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting—0
Dalek -- a deep-learning emulator for TARDIS—0
A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Daily Middle-Term Probabilistic Forecasting of Power Consumption in North-East England—0
DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation—0
A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency—0
D2KE: From Distance to Kernel and Embedding—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