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

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Showing 4551–4600 of 6748 papers

TitleStatusHype
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering—0
Application of Time Series Analysis to Traffic Accidents in Los Angeles—0
Topological Machine Learning for Multivariate Time SeriesCode0
A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers—0
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data—0
A Time Series Analysis of Emotional Loading in Central Bank Statements—0
Universal EEG Encoder for Learning Diverse Intelligent Tasks—0
Network Intrusion Detection based on LSTM and Feature Embedding—0
An Optimized and Energy-Efficient Parallel Implementation of Non-Iteratively Trained Recurrent Neural Networks—0
Revisiting Deep Architectures for Head Motion Prediction in 360° Videos—0
TimeCaps: Capturing Time Series Data With Capsule NetworksCode0
Correlative Channel-Aware Fusion for Multi-View Time Series Classification—0
High-Dimensional Forecasting in the Presence of Unit Roots and Cointegration—0
Functional Bayesian Filter—0
Differentiable Algorithm for Marginalising Changepoints—0
Modeling emotion in complex stories: the Stanford Emotional Narratives DatasetCode0
Economy Statistical Recurrent Units For Inferring Nonlinear Granger CausalityCode0
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values—0
Deep Reinforcement Learning for Trading—0
Simultaneous Implementation Features Extraction and Recognition Using C3D Network for WiFi-based Human Activity Recognition—0
On the separation of shape and temporal patterns in time series -Application to signature authentication-Code0
Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior—0
Multi-Scale RCNN Model for Financial Time-series Classification—0
Convolutional Mixture Density Recurrent Neural Network for Predicting User Location with WiFi Fingerprints—0
A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM—0
Robust Inference on Infinite and Growing Dimensional Time Series Regression—0
Shapelets for earthquake detection—0
A simulation of the insurance industry: The problem of risk model homogeneity—0
Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time SeriesCode0
Cell Line Classification Using Electric Cell-substrate Impedance Sensing (ECIS)—0
Challenges with Extreme Class-Imbalance and Temporal Coherence: A Study on Solar Flare Data—0
The dynamics of the stomatognathic system from 4D multimodal data—0
A Scrambled Method of Moments—0
Equivariant online predictions of non-stationary time series—0
Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationshipsCode0
Action Recognition Using Volumetric Motion RepresentationsCode0
Learning Weighted Submanifolds with Variational Autoencoders and Riemannian Variational Autoencoders—0
Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural NetworkCode0
Bayesian Filtering for Multi-period Mean-Variance Portfolio Selection—0
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data—0
Bayesian Recurrent Framework for Missing Data Imputation and Prediction with Clinical Time Series—0
Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-AttentionCode0
Benchmarking time series classification -- Functional data vs machine learning approachesCode0
Weather event severity prediction using buoy data and machine learning—0
Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen PredictionCode0
Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in HealthcareCode0
RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Series—0
SMART: Skeletal Motion Action Recognition aTtack—0
The Creation and Validation of Load Time Series for Synthetic Electric Power Systems—0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
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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