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 4751–4775 of 6748 papers

TitleStatusHype
Timage -- A Robust Time Series Classification PipelineCode0
AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids—0
Bifurcation Spiking Neural Network—0
sktime: A Unified Interface for Machine Learning with Time Series—0
Distributional conformal predictionCode0
Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning—0
Towards a Rigorous Evaluation of XAI Methods on Time Series—0
Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models—0
Spatiotemporal Attention Networks for Wind Power ForecastingCode0
Interpolation-Prediction Networks for Irregularly Sampled Time SeriesCode0
GENDIS: GENetic DIscovery of ShapeletsCode0
Meta-Learning for Few-Shot Time Series Classification—0
Activity recognition using ST-GCN with 3D motion data—0
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing—0
Using Clinical Notes with Time Series Data for ICU ManagementCode0
Explicit-Duration Markov Switching Models—0
Reinforcement Learning for Portfolio ManagementCode0
A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency—0
Multi-Year Vector Dynamic Time Warping Based Crop Mapping—0
Functional Annotation of Human Cognitive States using Graph Convolution Networks—0
Asset correlation estimation for inhomogeneous exposure pools—0
Photometric light curves classification with machine learning—0
LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal PatternsCode0
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data—0
Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms—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