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 4576–4600 of 6748 papers

TitleStatusHype
Outliagnostics: Visualizing Temporal Discrepancy in Outlying Signatures of Data Entries—0
Convolutional Conditional Neural ProcessesCode0
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelsCode3
Harnessing the power of Topological Data Analysis to detect change points in time seriesCode0
Large-Scale Characterization and Segmentation of Internet Path Delays with Infinite HMMsCode0
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural NetworksCode0
Deep Learning for Plasma Tomography and Disruption Prediction from Bolometer Data—0
Textual Data for Time Series Forecasting—0
Causal inference for climate change events from satellite image time series using computer vision and deep learning—0
Time Series Vector Autoregression Prediction of the Ecological Footprint based on Energy Parameters—0
High dimensional regression for regenerative time-series: an application to road traffic modelingCode0
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep StagingCode1
Inference of Binary Regime Models with Jump Discontinuities—0
Critical Transitions in Intensive Care Units: A Sepsis Case Study—0
MLAT: Metric Learning for kNN in Streaming Time Series—0
Self-attention for raw optical Satellite Time Series ClassificationCode0
Wasserstein total variation filtering—0
Winning the ICCV 2019 Learning to Drive Challenge—0
Study of the impact of climate change on precipitation in Paris area using method based on iterative multiscale dynamic time warping (IMS-DTW)—0
Order patterns, their variation and change points in financial time series and Brownian motion—0
You May Not Need Order in Time Series Forecasting—0
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems—0
Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community—0
Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis—0
Generalised learning of time-series: Ornstein-Uhlenbeck processes—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