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 4376–4400 of 6748 papers

TitleStatusHype
For2For: Learning to forecast from forecasts—0
IoT Network Behavioral Fingerprint Inference with Limited Network Trace for Cyber Investigation: A Meta Learning Approach—0
Relational State-Space Model for Stochastic Multi-Object Systems—0
Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learningCode0
Projection assisted Dynamic Mode Decomposition of large scale data—0
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models—0
Temporally Folded Convolutional Neural Networks for Sequence Forecasting—0
Forecasting NIFTY 50 benchmark Index using Seasonal ARIMA time series models—0
Supervised Hyperalignment for multi-subject fMRI data alignment—0
Explainable Deep Convolutional Candlestick LearnerCode1
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video—0
Discovering Nonlinear Relations with Minimum Predictive Information RegularizationCode1
Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering—0
Prediction of MRI Hardware Failures based on Image Features using Time Series Classification—0
Data Curves Clustering Using Common Patterns Detection—0
Temporal Tensor Transformation Network for Multivariate Time Series Prediction—0
Root Cause Detection Among Anomalous Time Series Using Temporal State AlignmentCode1
Biologically-Motivated Deep Learning Method using Hierarchical Competitive LearningCode0
Source Model Selection for Deep Learning in the Time Series DomainCode1
Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability—0
A Deep Structural Model for Analyzing Correlated Multivariate Time Series—0
Learnable Group Transform For Time-SeriesCode0
Cost-effective Interactive Attention Learning with Neural Attention ProcessCode0
Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations—0
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-timeCode0
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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