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 2951–2975 of 6748 papers

TitleStatusHype
Evaluation of deep learning models for multi-step ahead time series predictionCode1
Active multi-fidelity Bayesian online changepoint detectionCode1
Deep EHR Spotlight: a Framework and Mechanism to Highlight Events in Electronic Health Records for Explainable Predictions—0
Measuring frequency-dependent selection in culture—0
Intraday trading strategy based on time series and machine learning for Chinese stock market—0
Isolating the impact of trading on grid frequency fluctuations—0
An investigation of higher order moments of empirical financial data and the implications to risk—0
Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving NoiseCode0
Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability—0
Reframing demand forecasting: a two-fold approach for lumpy and intermittent demand—0
Neural ODE ProcessesCode1
Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery—0
Cryptocurrency Dynamics: Rodeo or Ascot?—0
Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications—0
Domain Specific Concept Drift Detectors for Predicting Financial Time Series—0
An Experimental Review on Deep Learning Architectures for Time Series ForecastingCode1
Handling Missing Observations with an RNN-based Prediction-Update Cycle—0
Markov Modeling of Time-Series Data using Symbolic Analysis—0
An Empirical Framework for Domain Generalization in Clinical SettingsCode1
Social Link Inference via Multi-View Matching Network from Spatio-Temporal Trajectories—0
Joint Parameter Discovery and Generative Modeling of Dynamic SystemsCode0
Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting -- Full version—0
Sparse Algorithms for Markovian Gaussian ProcessesCode0
Learning Time Series from Scale Information—0
Lossless compression with state space models using bits back codingCode0
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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