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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 351–375 of 6748 papers

TitleStatusHype
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time SeriesCode1
Dynamic Graph Learning-Neural Network for Multivariate Time Series ModelingCode1
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading RulesCode1
Dynamic Sparse Network for Time Series Classification: Learning What to "see''Code1
Axial-LOB: High-Frequency Trading with Axial AttentionCode1
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalsCode1
Deep ConvLSTM with self-attention for human activity decoding using wearablesCode1
General Evaluation for Instruction Conditioned Navigation using Dynamic Time WarpingCode1
Backdoor Attacks on Time Series: A Generative ApproachCode1
Benchmarking Deep Learning Interpretability in Time Series PredictionsCode1
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
Bayesian hierarchical stacking: Some models are (somewhere) usefulCode1
Deep Explicit Duration Switching Models for Time SeriesCode1
Deep and Confident Prediction for Time Series at UberCode1
Accelerating Recurrent Neural Networks for Gravitational Wave ExperimentsCode1
BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief PropagationCode1
A Multi-Scale Decomposition MLP-Mixer for Time Series AnalysisCode1
Adaptive Conformal Predictions for Time SeriesCode1
Benchmark time series data sets for PyTorch -- the torchtime packageCode1
Ensembles of Localised Models for Time Series ForecastingCode1
Deep Adaptive Input Normalization for Time Series ForecastingCode1
A Multi-view Multi-task Learning Framework for Multi-variate Time Series ForecastingCode1
High-Dimensional Granger Causality Tests with an Application to VIX and NewsCode1
Bilinear Input Normalization for Neural Networks in Financial ForecastingCode1
Deep Autoregressive Models with Spectral AttentionCode1
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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