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

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Showing 476–500 of 6748 papers

TitleStatusHype
Forecasting the Turkish Lira Exchange Rates through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?—0
Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape—0
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies—0
Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking—0
Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates—0
ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification—0
Estimating Driver Personality Traits from On-Road Driving Data—0
MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel MixingCode2
Weakly Supervised Anomaly Detection: A SurveyCode1
Dynamic and Stochastic Rational Behavior—0
High-Dimensional Granger Causality for Climatic Attribution—0
DeepVATS: Deep Visual Analytics for Time SeriesCode1
Short-Term Memory Convolutions—0
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks—0
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series—0
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining ApproachCode1
Unsupervised Deep Learning for IoT Time Series—0
CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series DataCode0
Market-Based Probability of Stock Returns—0
Noise-cleaning the precision matrix of fMRI time series—0
Tree-Based Learning on Amperometric Time Series Data Demonstrates High Accuracy for Classification—0
Identifiability of latent-variable and structural-equation models: from linear to nonlinear—0
Penalized Quasi-likelihood Estimation and Model Selection in Time Series Models with Parameters on the Boundary—0
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
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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