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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 301–350 of 6748 papers

TitleStatusHype
Deep Learning for Time Series Anomaly Detection: A SurveyCode1
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data ProcessingCode1
Deep Isolation Forest for Anomaly DetectionCode1
Deep Latent State Space Models for Time-Series GenerationCode1
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series DataCode1
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly DetectionCode1
Deep Learning-based Damage Mapping with InSAR Coherence Time SeriesCode1
Arbitrage-free neural-SDE market modelsCode1
Deep Dynamic Factor ModelsCode1
A Review of Graph Neural Networks and Their Applications in Power SystemsCode1
Deep Explicit Duration Switching Models for Time SeriesCode1
ARMA Cell: A Modular and Effective Approach for Neural Autoregressive ModelingCode1
Are we certain it's anomalous?Code1
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading RulesCode1
Deep Learning for Time Series Classification and Extrinsic Regression: A Current SurveyCode1
Deep reconstruction of strange attractors from time seriesCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
Aligning Time Series on Incomparable SpacesCode1
DeepMoD: Deep learning for Model Discovery in noisy dataCode1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series DataCode1
DeepSITH: Efficient Learning via Decomposition of What and When Across Time ScalesCode1
Deep Autoregressive Models with Spectral AttentionCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series ClassificationCode1
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence CaseCode1
DeepVARwT: Deep Learning for a VAR Model with TrendCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
A Synthetic Texas Power System with Time-Series Weather-Dependent Spatiotemporal ProfilesCode1
A Time-dependent SIR model for COVID-19 with Undetectable Infected PersonsCode1
Deep Adaptive Input Normalization for Time Series ForecastingCode1
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
Detecting Anomalies within Time Series using Local Neural TransformationsCode1
A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome PredictionCode1
Deep and Confident Prediction for Time Series at UberCode1
Attention based Multi-Modal New Product Sales Time-series ForecastingCode1
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Attentive Neural Controlled Differential Equations for Time-series Classification and ForecastingCode1
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingCode1
Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time WarpingCode1
AA-Forecast: Anomaly-Aware Forecast for Extreme EventsCode1
Automatic Change-Point Detection in Time Series via Deep LearningCode1
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly DetectionCode1
Discovering Nonlinear Relations with Minimum Predictive Information RegularizationCode1
Automated Evolutionary Approach for the Design of Composite Machine Learning PipelinesCode1
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICACode1
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsCode1
Accelerating Recurrent Neural Networks for Gravitational Wave ExperimentsCode1
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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