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 5926–5950 of 6748 papers

TitleStatusHype
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk PredictionCode0
SSDNet: State Space Decomposition Neural Network for Time Series ForecastingCode0
Kernel Change-point Detection with Auxiliary Deep Generative ModelsCode0
SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time SeriesCode0
SSIM -A Deep Learning Approach for Recovering Missing Time Series Sensor DataCode0
Discovering long term dependencies in noisy time series data using deep learningCode0
TiSAT: Time Series Anomaly TransformerCode0
Reservoir computing approaches for representation and classification of multivariate time seriesCode0
A Correlation Based Feature Representation for First-Person Activity RecognitionCode0
COVID-19 Time-series Prediction by Joint Dictionary Learning and Online NMFCode0
Dilated Convolutional Neural Networks for Time Series ForecastingCode0
TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNsCode0
An Information Theory Approach on Deciding Spectroscopic Follow UpsCode0
Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?Code0
On Improving Deep Reinforcement Learning for POMDPsCode0
A genetic algorithm to discover flexible motifs with supportCode0
k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image SequencesCode0
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time seriesCode0
StackVAE-G: An efficient and interpretable model for time series anomaly detectionCode0
Beyond Sparsity: Tree Regularization of Deep Models for InterpretabilityCode0
Knowledge Enhanced Neural Fashion Trend ForecastingCode0
Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel DataCode0
Online Change Point Detection for Weighted and Directed Random Dot Product GraphsCode0
Diffeomorphic Temporal Alignment NetsCode0
The Multi-Temporal Urban Development SpaceNet DatasetCode0
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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