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

Showing 67016748 of 6748 papers

TitleStatusHype
Transformers predicting the future. Applying attention in next-frame and time series forecastingCode0
Emergence of Functionally Differentiated Structures via Mutual Information Optimization in Recurrent Neural NetworksCode0
VisioRed: A Visualisation Tool for Interpretable Predictive MaintenanceCode0
Causal Discovery with Attention-Based Convolutional Neural NetworksCode0
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationCode0
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link PredictionCode0
Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent SpaceCode0
Solving the optimal stopping problem with reinforcement learning: an application in financial option exerciseCode0
Causal Discovery using Model Invariance through Knockoff InterventionsCode0
Multivariate Time Series Classification using Dilated Convolutional Neural NetworkCode0
Rapid training of quantum recurrent neural networksCode0
Multivariate Time Series Classification with WEASEL+MUSECode0
Time Series Prediction by Multi-task GPR with Spatiotemporal Information TransformationCode0
Multivariate Time Series Early Classification Across Channel and Time DimensionsCode0
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
Elastic Product Quantization for Time SeriesCode0
Elastic bands across the path: A new framework and methods to lower bound DTWCode0
Causal discovery for time series with latent confoundersCode0
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous VariablesCode0
Time Series Prediction for Food sustainabilityCode0
GP-VAE: Deep Probabilistic Time Series ImputationCode0
SOM-VAE: Interpretable Discrete Representation Learning on Time SeriesCode0
Efficient Matrix Profile Computation Using Different Distance FunctionsCode0
Real numbers, data science and chaos: How to fit any dataset with a single parameterCode0
Data Augmentation for Generating Synthetic Electrogastrogram Time SeriesCode0
Data-driven detrending of nonstationary fractal time series with echo state networksCode0
Multi-view Integration Learning for Irregularly-sampled Clinical Time SeriesCode0
Caulking the Leakage Effect in MEEG Source Connectivity AnalysisCode0
Real-Time Anomaly Detection for Streaming AnalyticsCode0
Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable DataCode0
Multi-VQG: Generating Engaging Questions for Multiple ImagesCode0
MASA: Motif-Aware State Assignment in Noisy Time Series DataCode0
Capturing the temporal constraints of gradual patternsCode0
Capturing Structure Implicitly from Time-Series having Limited DataCode0
Sparse Algorithms for Markovian Gaussian ProcessesCode0
Time Series Prediction for Graphs in Kernel and Dissimilarity SpacesCode0
ATCN: Resource-Efficient Processing of Time Series on EdgeCode0
Efficient learning of nonlinear prediction models with time-series privileged informationCode0
Transform-Invariant Non-Parametric Clustering of Covariance Matrices and its Application to Unsupervised Joint Segmentation and Action DiscoveryCode0
Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and Snapshot Time Series DataCode0
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded EmbeddingCode0
Time Series Prediction under Distribution Shift using Differentiable ForgettingCode0
Efficient Covariance Estimation from Temporal DataCode0
Capturing Actionable Dynamics with Structured Latent Ordinary Differential EquationsCode0
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time SeriesCode0
Deep Efficient Continuous Manifold Learning for Time Series ModelingCode0
N-BEATS neural network for mid-term electricity load forecastingCode0
Real-time Power System State Estimation and Forecasting via Deep Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified