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 6026–6050 of 6748 papers

TitleStatusHype
Investigation on the use of Hidden-Markov Models in automatic transcription of music—0
The MATLAB Toolbox SciXMiner: User's Manual and Programmer's Guide—0
Deep Multimodal Representation Learning from Temporal Data—0
On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models—0
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series PredictionCode0
Reservoir observers: Model-free inference of unmeasured variables in chaotic systems—0
Automated Diagnosis of Epilepsy Employing Multifractal Detrended Fluctuation Analysis Based Features—0
Online deforestation detection—0
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing DataCode0
Memory-Efficient Centroid Decomposition for Long Time SeriesCode0
Skeletonnet: Mining deep part features for 3-d action recognition—0
Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs—0
"Chaos" in energy and commodity markets: a controversial matter—0
Optimal Policies for Observing Time Series and Related Restless Bandit Problems—0
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series—0
Grouped Convolutional Neural Networks for Multivariate Time Series—0
Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings—0
Discovering Latent Covariance Structures for Multiple Time Series—0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network—0
Evidence of Self-Organization in Time Series of Capital Markets—0
Sparse Multi-Output Gaussian Processes for Medical Time Series PredictionCode0
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks—0
Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor Classification in Dynamic Time Warping Spaces—0
The Problem of Calibrating an Agent-Based Model of High-Frequency Trading—0
Independence clustering (without a matrix)—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