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

Showing 101125 of 6748 papers

TitleStatusHype
Probabilistic Time Series Forecasting with Implicit Quantile NetworksCode2
Closed-form Continuous-time Neural ModelsCode2
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series ForecastingCode2
AST: Audio Spectrogram TransformerCode2
Neural SDEs as Infinite-Dimensional GANsCode2
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series ForecastingCode2
Conformal prediction interval for dynamic time-seriesCode2
Deep learning for time series classificationCode2
TODS: An Automated Time Series Outlier Detection SystemCode2
TadGAN: Time Series Anomaly Detection Using Generative Adversarial NetworksCode2
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic ForecastingCode2
Liquid Time-constant NetworksCode2
Deep Learning for Time Series Forecasting: Tutorial and Literature SurveyCode2
TSFEL: Time Series Feature Extraction LibraryCode2
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing FlowsCode2
AR-Net: A simple Auto-Regressive Neural Network for time-seriesCode2
Bayesian Temporal Factorization for Multidimensional Time Series PredictionCode2
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series ForecastingCode2
Deep learning for time series classification: a reviewCode2
Optimal Transport for structured data with application on graphsCode2
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent NetworksCode2
LSTM-based Encoder-Decoder for Multi-sensor Anomaly DetectionCode2
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor LearningCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series ForecastingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified