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 6251–6300 of 6748 papers

TitleStatusHype
Discrete MDL Predicts in Total Variation—0
Discrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters—0
Discretizing Logged Interaction Data Biases Learning for Decision-Making—0
Discriminative Functional Connectivity Measures for Brain Decoding—0
Discriminative State Space Models—0
A new look at the anthropogenic global warming consensus: an econometric forecast based on the ARIMA model of paleoclimate series—0
Disease Trajectory Maps—0
Disk storage management for LHCb based on Data Popularity estimator—0
Dissection of Bitcoin's Multiscale Bubble History from January 2012 to February 2018—0
Dissimilarity-based Sparse Subset Selection—0
Distance-to-Mean Continuous Conditional Random Fields to Enhance Prediction Problem in Traffic Flow Data—0
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain—0
Distinguishing noise from chaos: objective versus subjective criteria using Horizontal Visibility Graph—0
Distinguishing Risk Preferences using Repeated Gambles—0
Distributed Estimation of Sparse Inverse Covariances—0
Distributed Learning and its Application for Time-Series Prediction—0
Distributed Online Learning with Multiple Kernels—0
Distributed Online Learning with Multiple Kernels—0
Distributed Reconstruction of Nonlinear Networks: An ADMM Approach—0
Distributed Supervised Learning using Neural Networks—0
Distributional Vector Autoregression: Eliciting Macro and Financial Dependence—0
Distribution estimation and change-point estimation for time series via DNN-based GANs—0
Distribution Regression for Sequential Data—0
District Wise Price Forecasting of Wheat in Pakistan using Deep Learning—0
Diverse Counterfactual Explanations for Anomaly Detection in Time Series—0
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification—0
DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization—0
DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting—0
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?—0
Does Deep Learning REALLY Outperform Non-deep Machine Learning for Clinical Prediction on Physiological Time Series?—0
Does Restraining End Effect Matter in EMD-Based Modeling Framework for Time Series Prediction? Some Experimental Evidences—0
Domain Generalization via Selective Consistency Regularization for Time Series Classification—0
Domain-guided Machine Learning for Remotely Sensed In-Season Crop Growth Estimation—0
Domain-invariant Feature Exploration for Domain Generalization—0
Domain Specific Concept Drift Detectors for Predicting Financial Time Series—0
Do Mature Economies Grow Exponentially?—0
DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena—0
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video—0
Don't overfit the history -- Recursive time series data augmentation—0
Double Diffusion Maps and their Latent Harmonics for Scientific Computations in Latent Space—0
Downsampling and geometric feature methods for EEG classification tasks with CNNs—0
Do Word Embeddings Really Understand Loughran-McDonald's Polarities?—0
DRAformer: Differentially Reconstructed Attention Transformer for Time-Series Forecasting—0
CORAL: Concept Drift Representation Learning for Co-evolving Time-series—0
Drift-Adjusted And Arbitrated Ensemble Framework For Time Series Forecasting—0
Drift Identification for Lévy alpha-Stable Stochastic Systems—0
Drifting states and synchronization induced chaos in autonomous networks of excitable neurons—0
Driver Identification Using Automobile Sensor Data from a Single Turn—0
Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification—0
Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches—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