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 2301–2325 of 6748 papers

TitleStatusHype
CDPS: Constrained DTW-Preserving Shapelets—0
Causal Triple Attention Time Series Forecasting—0
TS-BERT: A fusion model for Pre-trainning Time Series-Text Representations—0
Mixed-Memory RNNs for Learning Long-term Dependencies in Irregularly Sampled Time Series—0
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?—0
Selective Cross-Domain Consistency Regularization for Time Series Domain Generalization—0
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis—0
Tractable Dendritic RNNs for Identifying Unknown Nonlinear Dynamical Systems—0
Multi-Task Processes—0
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification—0
Modelling neuronal behaviour with time series regression: Recurrent Neural Networks on synthetic C. elegans data—0
Automatic Forecasting via Meta-Learning—0
Taking ROCKET on an efficiency mission: A distributed solution for fast and accurate multivariate time series classification—0
STRIC: Stacked Residuals of Interpretable Components for Time Series Anomaly Detection—0
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and ForecastingCode1
MECATS: Mixture-of-Experts for Probabilistic Forecasts of Aggregated Time Series—0
Huber Additive Models for Non-stationary Time Series Analysis—0
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution ShiftCode1
Coherence-based Label Propagation over Time Series for Accelerated Active Learning—0
Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs—0
Cluster-based Feature Importance Learning for Electronic Health Record Time-series—0
Loss meta-learning for forecasting—0
Information-Aware Time Series Meta-Contrastive Learning—0
Integrating Attention Feedback into the Recurrent Neural NetworkCode0
BLUnet: Arithmetic-free Inference with Bit-serialised Table Lookup Operation for Efficient Deep Neural Networks—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