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 426–450 of 6748 papers

TitleStatusHype
Generalization of Auto-Regressive Hidden Markov Models to Non-Linear Dynamics and Unit Quaternion Observation Space—0
LightCTS: A Lightweight Framework for Correlated Time Series ForecastingCode1
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference—0
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series ForecastingCode1
Time-varying Signals Recovery via Graph Neural Networks—0
Information Theory Inspired Pattern Analysis for Time-series Data—0
Learning Mixture Structure on Multi-Source Time Series for Probabilistic Forecasting—0
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles—0
The DeepCAR Method: Forecasting Time-Series Data That Have Change PointsCode0
Measuring city-scale green infrastructure drawdown dynamics using internet-connected sensors in Detroit—0
MVMTnet: A Multi-variate Multi-modal Transformer for Multi-class Classification of Cardiac Irregularities Using ECG Waveforms and Clinical NotesCode1
Enhancing Energy System Models Using Better Load Forecasts—0
Task-Oriented Prediction and Communication Co-Design for Haptic Communications—0
TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling and AI-Generated Arts—0
Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data—0
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather StationsCode1
FedST: Secure Federated Shapelet Transformation for Time Series Classification—0
Exploring the Advantages of Transformers for High-Frequency TradingCode1
Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional StrategiesCode1
Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting—0
Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early ClassificationCode0
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification—0
CNTS: Cooperative Network for Time SeriesCode0
Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictions—0
Ergodic characterization of non-ergodic anomalous diffusion processes—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