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 326–350 of 6748 papers

TitleStatusHype
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient EncoderCode0
Switching Autoregressive Low-rank Tensor ModelsCode0
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models—0
An OPC UA-based industrial Big Data architecture—0
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time SeriesCode2
Improving Position Encoding of Transformers for Multivariate Time Series ClassificationCode1
Evaluating generation of chaotic time series by convolutional generative adversarial networksCode0
Bias, Consistency, and Partisanship in U.S. Asylum Cases: A Machine Learning Analysis of Extraneous Factors in Immigration Court Decisions—0
SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise—0
Sliding Window Sum Algorithms for Deep Neural Networks—0
Comfort Foods and Community Connectedness: Investigating Diet Change during COVID-19 Using YouTube Videos on Twitter—0
Predicting Unplanned Readmissions in the Intensive Care Unit: A Multimodality Evaluation—0
Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary PerspectiveCode0
Ordinal time series analysis with the R package otsfeatures—0
Non-destructive Fault Diagnosis of Electronic Interconnects by Learning Signal Patterns of Reflection Coefficient in the Frequency Domain—0
Filling out the missing gaps: Time Series Imputation with Semi-Supervised Learning—0
SMPConv: Self-moving Point Representations for Continuous ConvolutionCode1
OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And ForecastingCode1
The short- and long-term determinants of fertility in Uruguay—0
Time Series Segmentation Applied to a New Data Set for Mobile Sensing of Human ActivitiesCode1
UniTS: A Universal Time Series Analysis Framework Powered by Self-Supervised Representation LearningCode1
Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning—0
Delay-SDE-net: A deep learning approach for time series modelling with memory and uncertainty estimatesCode0
Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data—0
FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting—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