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 1351–1375 of 6748 papers

TitleStatusHype
Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets—0
DRAformer: Differentially Reconstructed Attention Transformer for Time-Series Forecasting—0
ProActive: Self-Attentive Temporal Point Process Flows for Activity SequencesCode0
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformersCode1
Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review—0
Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition—0
Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Unsupervised Learning—0
It's a super deal -- train recurrent network on noisy data and get smooth prediction free—0
Exploring Predictive States via Cantor Embeddings and Wasserstein Distance—0
VitalDBCode1
Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention—0
Classification of Stochastic Processes with Topological Data Analysis—0
Multivariate backtests and copulas for risk evaluation—0
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series ForecastingCode1
Motiflets -- Simple and Accurate Detection of Motifs in Time SeriesCode1
Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting—0
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamicsCode1
TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study)Code1
On the balance between the training time and interpretability of neural ODE for time series modelling—0
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection—0
Forecasting COVID- 19 cases using Statistical Models and Ontology-based Semantic Modelling: A real time data analytics approach—0
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data—0
DDPG based on multi-scale strokes for financial time series trading strategy—0
Using Connectome Features to Constrain Echo State Networks—0
Causal impact of severe events on electricity demand: The case of COVID-19 in Japan—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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