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 376–400 of 6748 papers

TitleStatusHype
Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full VersionCode1
HYDRA: Competing convolutional kernels for fast and accurate time series classificationCode1
Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix FactorizationCode1
Learning Whole Heart Mesh Generation From Patient Images For Computational SimulationsCode1
WOODS: Benchmarks for Out-of-Distribution Generalization in Time SeriesCode1
Generalized Classification of Satellite Image Time Series with Thermal Positional EncodingCode1
SepTr: Separable Transformer for Audio Spectrogram ProcessingCode1
Euler State Networks: Non-dissipative Reservoir ComputingCode1
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time SeriesCode1
DEPTS: Deep Expansion Learning for Periodic Time Series ForecastingCode1
Wasserstein Adversarial Transformer for Cloud Workload PredictionCode1
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
S-Rocket: Selective Random Convolution Kernels for Time Series ClassificationCode1
DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local ExplanationsCode1
ES-dRNN with Dynamic Attention for Short-Term Load ForecastingCode1
High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation LearningCode1
Wearable Sensor-Based Human Activity Recognition with Transformer ModelCode1
Integrated multimodal artificial intelligence framework for healthcare applicationsCode1
Robust Probabilistic Time Series ForecastingCode1
Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series ForecastingCode1
Learning Fast and Slow for Online Time Series ForecastingCode1
Combating Distribution Shift for Accurate Time Series Forecasting via HypernetworksCode1
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed GraphsCode1
Signal Decomposition Using Masked Proximal OperatorsCode1
Ensemble Conformalized Quantile Regression for Probabilistic 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