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 3576–3600 of 6748 papers

TitleStatusHype
Gene Regulatory Network Inference with Latent Force Models—0
Direct Signal Separation Via Extraction of Local Frequencies with Adaptive Time-Varying Parameters—0
Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution—0
Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices—0
Mining and modeling complex leadership-followership dynamics of movement data—0
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series—0
Bayesian Feature Selection in Joint Quantile Time Series Analysis—0
Online Neural Networks for Change-Point DetectionCode1
From Time Series to Euclidean Spaces: On Spatial Transformations for Temporal Clustering—0
Active Tuning—0
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification—0
An Evaluation of Classification Methods for 3D Printing Time-Series Data—0
Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information—0
Citation Sentiment Changes AnalysisCode0
Universal time-series forecasting with mixture predictors—0
Deep learning for time series classificationCode2
A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction—0
AAMDRL: Augmented Asset Management with Deep Reinforcement Learning—0
Uncovering Feature Interdependencies in High-Noise Environments with Stepwise Lookahead Decision Forests—0
Few-shot Learning for Time-series Forecasting—0
Concurrent Neural Network : A model of competition between times seriesCode0
Go with the FLOW: Visualizing spatiotemporal dynamics in optical widefield calcium imaging—0
EEG to fMRI Synthesis: Is Deep Learning a candidate?—0
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
Neural CDEs for Long Time Series via the Log-ODE Method—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