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 3726–3750 of 6748 papers

TitleStatusHype
The Canonical Interval Forest (CIF) Classifier for Time Series Classification—0
Exact Tests for Offline Changepoint Detection in Multichannel Binary and Count Data with Application to Networks—0
Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting—0
Reforming the State-Based Forward Guidance through Wage Growth Rate Threshold: Evidence from FRB/US SimulationsCode0
Augmenting Neural Differential Equations to Model Unknown Dynamical Systems with Incomplete State InformationCode0
MTHetGNN: A Heterogeneous Graph Embedding Framework for Multivariate Time Series Forecasting—0
Long vs Short Time Scales: the Rough Dilemma and Beyond—0
Generative Adversarial Networks for Spatio-temporal Data: A Survey—0
Selecting Data Adaptive Learner from Multiple Deep Learners using Bayesian Networks—0
RTFN: Robust Temporal Feature Network—0
Predicting Future Sales of Retail Products using Machine Learning—0
A Formally Robust Time Series Distance Metric—0
Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting—0
Extension of causal decomposition in the mutual complex dynamic process—0
Deep Neural Networks for automatic extraction of features in time series satellite images—0
HiPPO: Recurrent Memory with Optimal Polynomial ProjectionsCode1
Learning from Irregularly-Sampled Time Series: A Missing Data PerspectiveCode1
Wavelet Denoising and Attention-based RNN-ARIMA Model to Predict Forex Price—0
Gait complexity assessed by detrended fluctuation analysis is sensitive to inconsistencies in stride time series: A modeling study—0
Neural Network-based Automatic Factor Construction—0
Image Processing Tools for Financial Time Series Classification—0
Spectral Processing of COVID-19 Time-Series DataCode0
A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models—0
Learning low-frequency temporal patterns for quantitative tradingCode0
Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional neural networksCode0
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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