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
Robust Multimodal Fusion for Human Activity Recognition—0
Self-contained Beta-with-Spikes Approximation for Inference Under a Wright-Fisher Model—0
Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with LoopsCode0
A Kalman Filter Framework for Resolving 3D Displacement Field Time Series By Combining Multitrack Multitemporal InSAR and GNSS Horizontal Velocities—0
TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT—0
eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)—0
Time series anomaly detection with reconstruction-based state-space modelsCode0
Robust Dominant Periodicity Detection for Time Series with Missing Data—0
Data Augmentation for Generating Synthetic Electrogastrogram Time SeriesCode0
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders—0
Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent SpaceCode0
Spectral learning of Bernoulli linear dynamical systems modelsCode0
Anamnesic Neural Differential Equations with Orthogonal Polynomial ProjectionsCode0
Reservoir computing based on solitary-like waves dynamics of film flows: a proof of concept—0
Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming—0
Building Floorspace in China: A Dataset and Learning Pipeline—0
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time SeriesCode0
Multi-Task Self-Supervised Time-Series Representation Learning—0
Interpretable System Identification and Long-term Prediction on Time-Series Data—0
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series—0
Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms—0
Your time series is worth a binary image: machine vision assisted deep framework for time series forecastingCode0
Edge computing on TPU for brain implant signal analysisCode0
Learning Hidden Markov Models Using Conditional Samples—0
Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach—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