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 6051–6075 of 6748 papers

TitleStatusHype
Fast Online Deconvolution of Calcium Imaging DataCode0
Conditional Time Series Forecasting with Convolutional Neural NetworksCode0
Autoregressive Convolutional Neural Networks for Asynchronous Time SeriesCode0
Locally embedded presages of global network bursts—0
A log-linear time algorithm for constrained changepoint detectionCode0
Qualitative Assessment of Recurrent Human Motion—0
A time series distance measure for efficient clustering of input output signals by their underlying dynamics—0
Network Inference via the Time-Varying Graphical LassoCode0
A Statistical Machine Learning Approach to Yield Curve Forecasting—0
A review of two decades of correlations, hierarchies, networks and clustering in financial markets—0
Modeling non-stationarities in high-frequency financial time series—0
Co-evolutionary multi-task learning for dynamic time series predictionCode0
Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis—0
Fractal approach towards power-law coherency to measure cross-correlations between time series—0
Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment—0
Time-Series Adaptive Estimation of Vaccination Uptake Using Web Search Queries—0
A Goal-Based Movement Model for Continuous Multi-Agent Tasks—0
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational CostsCode0
Structural Change in (Economic) Time Series—0
Detecting and modelling delayed density-dependence in abundance time series of a small mammal (Didelphis aurita)—0
Crossmatching variable objects with the Gaia data—0
Coresets for Kernel Regression—0
Similarity Preserving Representation Learning for Time Series Clustering—0
Robust Clustering for Time Series Using Spectral Densities and Functional Data Analysis—0
Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data—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