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 3201–3250 of 6748 papers

TitleStatusHype
Improved Grey System Models for Predicting Traffic Parameters—0
Improved Modeling of Complex Systems Using Hybrid Physics/Machine Learning/Stochastic Models—0
Causal Digital Twin from Multi-channel IoT—0
Improved PAC-Bayesian Bounds for Linear Regression—0
Improved Prediction and Network Estimation Using the Monotone Single Index Multi-variate Autoregressive Model—0
Improved Predictive Deep Temporal Neural Networks with Trend Filtering—0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)—0
Improvement of Flood Extent Representation with Remote Sensing Data and Data Assimilation—0
Curriculum Learning in Deep Neural Networks for Financial Forecasting—0
Emotional Expression Classification using Time-Series Kernels—0
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis—0
Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks—0
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems—0
Causal Consistency of Structural Equation Models—0
Improving Convolutional Neural Networks for Fault Diagnosis by Assimilating Global Features—0
D2KE: From Distance to Kernel and Embedding—0
Causal Compression—0
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition—0
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning—0
Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time—0
Embedding Symbolic Temporal Knowledge into Deep Sequential Models—0
Improving MF-DFA model with applications in precious metals market—0
Dalek -- a deep-learning emulator for TARDIS—0
Improving Neuroevolution Using Island Extinction and Repopulation—0
Improving Optimization for Models With Continuous Symmetry Breaking—0
Improving Optimization in Models With Continuous Symmetry Breaking—0
A Novel Deep Parallel Time-series Relation Network for Fault Diagnosis—0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness—0
Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection—0
Improving self-supervised pretraining models for epileptic seizure detection from EEG data—0
Improving Solar Flare Prediction by Time Series Outlier Detection—0
Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary—0
A Graph Signal Processing Approach For Real-Time Traffic Prediction In Transportation Networks—0
Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text—0
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation—0
Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility—0
Improving the quality control of seismic data through active learning—0
Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks—0
Improving the spectral resolution of fMRI signals through the temporal de-correlation approach—0
Improving the Thermal Infrared Monitoring of Volcanoes: A Deep Learning Approach for Intermittent Image Series—0
Improving Time Series Classification Algorithms Using Octave-Convolutional Layers—0
Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis—0
Forecasting, Causality, and Impulse Response with Neural Vector Autoregressions—0
Data Augmentation for Multivariate Time Series Classification: An Experimental Study—0
Imputation of Clinical Covariates in Time Series—0
Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic—0
Data Augmentation techniques in time series domain: A survey and taxonomy—0
Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique—0
Causal-based Time Series Domain Generalization for Vehicle Intention Prediction—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