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 2201–2225 of 6748 papers

TitleStatusHype
Adversarial attacks against Bayesian forecasting dynamic models—0
Power Line Communication and Sensing Using Time Series Forecasting—0
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!—0
Random Feature Approximation for Online Nonlinear Graph Topology Identification—0
Graph-based Local Climate Classification in Iran—0
SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative ArcsCode1
Towards Better Long-range Time Series Forecasting using Generative Adversarial Networks—0
Using Clinical Drug Representations for Improving Mortality and Length of Stay PredictionsCode0
A novel stochastic model based on echo state networks for hydrological time series forecasting—0
The elastic origins of tail asymmetry—0
Semimartingale and continuous-time Markov chain approximation for rough stochastic local volatility models—0
SleepPriorCL: Contrastive Representation Learning with Prior Knowledge-based Positive Mining and Adaptive Temperature for Sleep Staging—0
Nonlinear proper orthogonal decomposition for convection-dominated flowsCode1
Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction—0
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel SizesCode1
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time SeriesCode1
Probabilistic Time Series Forecasts with Autoregressive Transformation Models—0
Integrating Fréchet distance and AI reveals the evolutionary trajectory and origin of SARS-CoV-2—0
A Two-layer Approach for Estimating Behind-the-Meter PV Generation Using Smart Meter Data—0
On the difficulty of learning chaotic dynamics with RNNsCode1
Time Series Clustering for Human Behavior Pattern Mining—0
A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series Data—0
On Adversarial Vulnerability of PHM algorithms: An Initial Study—0
IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance—0
Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series ForecastingCode0
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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