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 1001–1025 of 6748 papers

TitleStatusHype
A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification—0
Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification—0
QIXAI: A Quantum-Inspired Framework for Enhancing Classical and Quantum Model Transparency and Understanding—0
HiPPO-KAN: Efficient KAN Model for Time Series Analysis—0
On the Regularization of Learnable Embeddings for Time Series Processing—0
Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal DynamicsCode0
Revisited Large Language Model for Time Series Analysis through Modality Alignment—0
Building a Multivariate Time Series Benchmarking Datasets Inspired by Natural Language Processing (NLP)—0
Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification—0
Generalizable autoregressive modeling of time series through functional narratives—0
Task-oriented Time Series Imputation Evaluation via Generalized RepresentersCode0
Scintillation pulse characterization with spectrum-inspired temporal neural networks: case studies on particle detector signals—0
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs—0
Training-free LLM-generated Text Detection by Mining Token Probability Sequences—0
Local Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series ForecastingCode0
BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge DistillationCode0
SEN12-WATER: A New Dataset for Hydrological Applications and its Benchmarking—0
TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models—0
Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining—0
Exploring Representations and Interventions in Time Series Foundation Models—0
A point process approach for the classification of noisy calcium imaging data—0
Weather Prediction Using CNN-LSTM for Time Series Analysis: A Case Study on Delhi Temperature Data—0
Second-order difference subspace—0
Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series—0
An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis—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