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 4851–4875 of 6748 papers

TitleStatusHype
Forecasting high-dimensional dynamics exploiting suboptimal embeddings—0
Maximum Entropy approach to multivariate time series randomization—0
Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting—0
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization—0
Neural Dynamics Discovery via Gaussian Process Recurrent Neural NetworksCode0
Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model—0
Fast Convolutive Nonnegative Matrix Factorization Through Coordinate and Block Coordinate Updates—0
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series ForecastingCode2
A multifactorial evaluation framework for gene regulatory network reconstruction—0
Anomaly Subsequence Detection with Dynamic Local Density for Time Series—0
A 1d convolutional network for leaf and time series classificationCode0
An Improvement of PAA on Trend-Based Approximation for Time Series—0
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data—0
Teaching DNNs to design fast fashion—0
Sparsity-Assisted Signal Denoising and Pattern Recognition in Time-Series DataCode0
Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection—0
Clustering piecewise stationary processes—0
TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series ClassificationCode0
Gesture Recognition in RGB Videos UsingHuman Body Keypoints and Dynamic Time WarpingCode0
Visualizing High Dimensional Dynamical Processes—0
Traffic Flow Combination Forecasting Method Based on Improved LSTM and ARIMA—0
A Self-supervised Approach to Hierarchical Forecasting with Applications to Groupwise Synthetic Controls—0
Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network—0
Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent UnitsCode0
Metaheuristics optimized feedforward neural networks for efficient stock price prediction—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