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 2676–2700 of 6748 papers

TitleStatusHype
Finding Patterns in Visualized Data by Adding Redundant Visual Information—0
Causality and Generalizability: Identifiability and Learning Methods—0
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks—0
Fine-grained Pattern Matching Over Streaming Time Series—0
A Hybrid Approach on Conditional GAN for Portfolio Analysis—0
From Generalization Analysis to Optimization Designs for State Space Models—0
Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures—0
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes—0
Causality and Correlations between BSE and NYSE indexes: A Janus Faced Relationship—0
Fitting Sparse Markov Models to Categorical Time Series Using Regularization—0
Combining Generative and Discriminative Neural Networks for Sleep Stages Classification—0
Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study—0
Causal Inference via Kernel Deviance Measures—0
FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data—0
A Novel Markov Model for Near-Term Railway Delay Prediction—0
Flexible conditional density estimation for time series—0
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series—0
Flexible Transmitter Network—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers—0
A regression model with a hidden logistic process for feature extraction from time series—0
Combining Sentinel-1 and Sentinel-2 Time Series via RNN for object-based land cover classification—0
Flow Forecast: A deep learning for time series forecasting, classification, and anomaly detection framework built in PyTorch—0
Frequency Domain Compact 3D Convolutional Neural Networks—0
Causal Inference from Slowly Varying Nonstationary Processes—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