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 4426–4450 of 6748 papers

TitleStatusHype
On the Metrics and Adaptation Methods for Domain Divergences of sEMG-based Gesture RecognitionCode0
Feature engineering workflow for activity recognition from synchronized inertial measurement unitsCode0
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks—0
Water Supply Prediction Based on Initialized Attention Residual Network—0
A posteriori Trading-inspired Model-free Time Series Segmentation—0
Applying Deep Learning to Detect Traffic Accidents in Real Time Using Spatiotemporal Sequential Data—0
High-Dimensional Granger Causality Tests with an Application to VIX and NewsCode1
CORAD: Correlation-Aware Compression of Massive Time Series using Sparse Dictionary CodingCode0
Modified Computation of Correlation Integral for Analyzing Epileptic Signals—0
Containment strategies and statistical measures for the control of Bovine Viral Diarrhea spread in livestock trade networks—0
Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19—0
GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Lining—0
Deteção de estruturas permanentes a partir de dados de séries temporais Sentinel 1 e 2—0
Classification des Séries Temporelles Incertaines par Transformation Shapelet—0
The Wasserstein-Fourier Distance for Stationary Time SeriesCode0
Towards Better Forecasting by Fusing Near and Distant Future VisionsCode0
Hidden Markov Model: Tutorial—0
Diffeomorphic Temporal Alignment NetsCode0
Machine learning models show similar performance to Renewables.ninja for generation of long-term wind power time series even without location information—0
An empirical study of neural networks for trend detection in time series—0
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers—0
Open-domain Event Extraction and Embedding for Natural Gas Market PredictionCode0
Automatic Financial Feature Construction—0
Data Exploration and Validation on dense knowledge graphs for biomedical research—0
Improved PAC-Bayesian Bounds for Linear Regression—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