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 4276–4300 of 6748 papers

TitleStatusHype
Geometric Fusion via Joint Delay Embeddings—0
Block Hankel Tensor ARIMA for Multiple Short Time Series ForecastingCode0
Multivariate time-series modeling with generative neural networks—0
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks—0
Adaptive Transmit Waveform Design using Multi-Tone Sinusoidal Frequency Modulation—0
Modeling Continuous Stochastic Processes with Dynamic Normalizing FlowsCode1
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classificationCode1
Variational Hyper RNN for Sequence Modeling—0
Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural NetworksCode0
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
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks—0
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity DetectionCode1
Forecasting Realized Volatility Matrix With Copula-Based Models—0
SummerTime: Variable-length Time SeriesSummarization with Applications to PhysicalActivity Analysis—0
Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural NetworkCode1
Forecasting Foreign Exchange Rate: A Multivariate Comparative Analysis between Traditional Econometric, Contemporary Machine Learning & Deep Learning TechniquesCode1
Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case—0
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting—0
Dynamic Graph Learning based on Graph Laplacian—0
Controlled time series generation for automotive software-in-the-loop testing using GANs—0
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing FlowsCode2
Deep reconstruction of strange attractors from time seriesCode1
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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