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 201–225 of 6748 papers

TitleStatusHype
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for MedicineCode1
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time SeriesCode1
Lightweight Neural Architecture Search for Temporal Convolutional Networks at the EdgeCode1
WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series ClassificationCode1
StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time SeriesCode1
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological DataCode1
Ti-MAE: Self-Supervised Masked Time Series AutoencodersCode1
Diffusion-based Conditional ECG Generation with Structured State Space ModelsCode1
A Transformer-based Diffusion Probabilistic Model for Heart Rate and Blood Pressure Forecasting in Intensive Care UnitCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEGCode1
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological OrderCode1
Deep Latent State Space Models for Time-Series GenerationCode1
Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode DecompositionCode1
Dynamic Sparse Network for Time Series Classification: Learning What to "see''Code1
FedTADBench: Federated Time-Series Anomaly Detection BenchmarkCode1
Convolution-enhanced Evolving Attention NetworksCode1
Temporal Saliency Detection Towards Explainable Transformer-based Timeseries ForecastingCode1
First De-Trend then Attend: Rethinking Attention for Time-Series ForecastingCode1
Integrating Multimodal Data for Joint Generative Modeling of Complex DynamicsCode1
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal ModelingCode1
Multi-Dimensional Self Attention based Approach for Remaining Useful Life EstimationCode1
Phase2vec: Dynamical systems embedding with a physics-informed convolutional networkCode1
Sequential Predictive Conformal Inference for Time SeriesCode1
Copula Conformal Prediction for Multi-step Time Series ForecastingCode1
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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