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 4026–4050 of 6748 papers

TitleStatusHype
Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market—0
Transformer-Based Self-Supervised Learning for Emotion Recognition—0
Transformer Encoder and Multi-features Time2Vec for Financial Prediction—0
Transformers in Time-series Analysis: A Tutorial—0
Rewiring the Transformer with Depth-Wise LSTMs—0
Probabilistic Time Series Forecasts with Autoregressive Transformation Models—0
Transient Classification in low SNR Gravitational Wave data using Deep Learning—0
Transitional Dynamics of the Saving Rate and Economic Growth—0
TransTCN: An Attention-based TCN Framework for Sequential Modeling—0
Travel time prediction for congested freeways with a dynamic linear model—0
Tree-Based Learning on Amperometric Time Series Data Demonstrates High Accuracy for Classification—0
TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting—0
Tree-structured Gaussian Process Approximations—0
TrendNets: Mapping Emerging Research Trends From Dynamic Co-Word Networks via Sparse Representation—0
Triadic Temporal Exponential Random Graph Models (TTERGM)—0
Triad State Space Construction for Chaotic Signal Classification with Deep Learning—0
Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version—0
Triple Simplex Matrix Completion for Expense Forecasting—0
Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications—0
Trojan Horse Hunt in Time Series Forecasting for Space Operations—0
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy—0
TS3IM: Unveiling Structural Similarity in Time Series through Image Similarity Assessment Insights—0
TS-BERT: A fusion model for Pre-trainning Time Series-Text Representations—0
TsFeX: Contact Tracing Model using Time Series Feature Extraction and Gradient Boosting—0
Tsformer: Time series Transformer for tourism demand forecasting—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