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

Showing 551600 of 6748 papers

TitleStatusHype
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
CAMul: Calibrated and Accurate Multi-view Time-Series ForecastingCode1
Closed-Form Diffeomorphic Transformations for Time Series AlignmentCode1
Calibration of Google Trends Time SeriesCode1
Color-aware two-branch DCNN for efficient plant disease classificationCode1
The Signature Kernel is the solution of a Goursat PDECode1
Conditional GAN for timeseries generationCode1
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural NetworksCode1
Neural graphical modelling in continuous-time: consistency guarantees and algorithmsCode1
AGNet: Weighing Black Holes with Deep LearningCode1
Continual Transformers: Redundancy-Free Attention for Online InferenceCode1
AGNet: Weighing Black Holes with Machine LearningCode1
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential EquationsCode1
Contrastive Learning for Unsupervised Domain Adaptation of Time SeriesCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
Can LLMs Understand Time Series Anomalies?Code1
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal BootstrappingCode1
Cost-effective Interactive Attention Learning with Neural Attention ProcessesCode1
COT-GAN: Generating Sequential Data via Causal Optimal TransportCode1
COVID-19 Data Analysis and Forecasting: Algeria and the WorldCode1
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential EquationsCode1
Crop mapping from image time series: deep learning with multi-scale label hierarchiesCode1
Data Generating Process to Evaluate Causal Discovery Techniques for Time Series DataCode1
Data Normalization for Bilinear Structures in High-Frequency Financial Time-seriesCode1
catch22: CAnonical Time-series CHaracteristicsCode1
dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series ClassificationCode1
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse EffectsCode1
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval PredictorsCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Deep and Confident Prediction for Time Series at UberCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
Deep ConvLSTM with self-attention for human activity decoding using wearablesCode1
Deep Dynamic Factor ModelsCode1
Deep Explicit Duration Switching Models for Time SeriesCode1
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Deep Latent State Space Models for Time-Series GenerationCode1
BolT: Fused Window Transformers for fMRI Time Series AnalysisCode1
Bilinear Input Normalization for Neural Networks in Financial ForecastingCode1
Adaptive Conformal Predictions for Time SeriesCode1
Deep Learning Statistical ArbitrageCode1
Deep Semi-Supervised Learning for Time Series ClassificationCode1
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data ProcessingCode1
Accelerating Recurrent Neural Networks for Gravitational Wave ExperimentsCode1
Deep Switching Auto-Regressive Factorization:Application to Time Series ForecastingCode1
Deeptime: a Python library for machine learning dynamical models from time series dataCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
DeepVATS: Deep Visual Analytics for Time SeriesCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified