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 4376–4400 of 6748 papers

TitleStatusHype
Evaluation of Temporal Complexity Reduction Techniques Applied to Storage Expansion Planning in Power System Models—0
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks—0
Geometric Fusion via Joint Delay Embeddings—0
Multivariate time-series modeling with generative neural networks—0
Block Hankel Tensor ARIMA for Multiple Short Time Series ForecastingCode0
Variational Hyper RNN for Sequence Modeling—0
Adaptive Transmit Waveform Design using Multi-Tone Sinusoidal Frequency Modulation—0
A New Unified Deep Learning Approach with Decomposition-Reconstruction-Ensemble Framework for Time Series Forecasting—0
Longitudinal Support Vector Machines for High Dimensional Time Series—0
Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural NetworksCode0
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks—0
Forecasting Realized Volatility Matrix With Copula-Based Models—0
SummerTime: Variable-length Time SeriesSummarization with Applications to PhysicalActivity Analysis—0
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting—0
Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case—0
Dynamic Graph Learning based on Graph Laplacian—0
Controlled time series generation for automotive software-in-the-loop testing using GANs—0
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition—0
Fully convolutional networks for structural health monitoring through multivariate time series classification—0
Online Learning of the Kalman Filter with Logarithmic Regret—0
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals—0
Gaussian process imputation of multiple financial series—0
Exact Indexing of Time Series under Dynamic Time Warping—0
On the statistics of scaling exponents and the Multiscaling Value at Risk—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