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 1701–1725 of 6748 papers

TitleStatusHype
Exploring Physical-Based Constraints in Short-Term Load Forecasting: A Defense Mechanism Against Cyberattack—0
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets—0
S-Rocket: Selective Random Convolution Kernels for Time Series ClassificationCode1
Automated Few-Shot Time Series Forecasting based on Bi-level Programming—0
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
Multivariate Time Series Forecasting with Latent Graph Inference—0
KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High Solar Scenarios—0
Deep Sequence Modeling for Pressure Controlled Mechanical VentilationCode0
Interpretable Latent Variables in Deep State Space Models—0
Comparison of LSTM autoencoder based deep learning enabled Bayesian inference using two time series reconstruction approaches—0
Bayesian Spillover Graphs for Dynamic NetworksCode0
Deep Q-network using reservoir computing with multi-layered readout—0
DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local ExplanationsCode1
Early Time-Series Classification Algorithms: An Empirical Comparison—0
Calculation of Sub-bands 1,2,5,6 for 64-Point Complex FFT and Its extension to N (=2^N) Point FFT—0
ES-dRNN with Dynamic Attention for Short-Term Load ForecastingCode1
Predicting the temporal dynamics of turbulent channels through deep learning—0
Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting—0
High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation LearningCode1
Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Prediction—0
Wearable Sensor-Based Human Activity Recognition with Transformer ModelCode1
Uncovering the dynamic effects of DEX treatment on lung cancer by integrating bioinformatic inference and multiscale modeling of scRNA-seq and proteomics data—0
Path sampling of recurrent neural networks by incorporating known physics—0
Molecular Dynamics of Polymer-lipids in Solution from Supervised Machine Learning—0
FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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