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 4551–4600 of 6748 papers

TitleStatusHype
Hierarchical Clustering for Smart Meter Electricity Loads based on Quantile Autocovariances—0
Discovering Invariances in Healthcare Neural Networks—0
An Information Theory Approach on Deciding Spectroscopic Follow UpsCode0
Architectural Tricks for Deep Learning in Remote Photoplethysmography—0
Deep Learning for Stock Selection Based on High Frequency Price-Volume Data—0
Deep Hedging: Learning to Simulate Equity Option MarketsCode0
Dynamic Time Warp Convolutional Networks—0
Seasonally-Adjusted Auto-Regression of Vector Time Series—0
Optimal Transport Based Change Point Detection and Time Series Segment Clustering—0
Novel semi-metrics for multivariate change point analysis and anomaly detection—0
Online Debiasing for Adaptively Collected High-dimensional Data with Applications to Time Series Analysis—0
Framework for Inferring Following Strategies from Time Series of Movement DataCode0
Application of Gaussian Process Regression to Koopman Mode Decomposition for Noisy Dynamic Data—0
DSANet: Dual Self-Attention Network for Multivariate Time Series ForecastingCode0
Generalizing to unseen domains via distribution matchingCode0
Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning—0
Variational Bayesian inference of hidden stochastic processes with unknown parameters—0
Decoding of visual-related information from the human EEG using an end-to-end deep learning approach—0
Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Data—0
Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings—0
Identifying Predictive Causal Factors from News Streams—0
Research and application of time series algorithms in centralized purchasing data—0
LFZip: Lossy compression of multivariate floating-point time series data via improved predictionCode0
Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis—0
Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devicesCode0
Outliagnostics: Visualizing Temporal Discrepancy in Outlying Signatures of Data Entries—0
Convolutional Conditional Neural ProcessesCode0
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelsCode3
Harnessing the power of Topological Data Analysis to detect change points in time seriesCode0
Large-Scale Characterization and Segmentation of Internet Path Delays with Infinite HMMsCode0
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural NetworksCode0
Deep Learning for Plasma Tomography and Disruption Prediction from Bolometer Data—0
Textual Data for Time Series Forecasting—0
Causal inference for climate change events from satellite image time series using computer vision and deep learning—0
Time Series Vector Autoregression Prediction of the Ecological Footprint based on Energy Parameters—0
High dimensional regression for regenerative time-series: an application to road traffic modelingCode0
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep StagingCode1
Inference of Binary Regime Models with Jump Discontinuities—0
Critical Transitions in Intensive Care Units: A Sepsis Case Study—0
MLAT: Metric Learning for kNN in Streaming Time Series—0
Self-attention for raw optical Satellite Time Series ClassificationCode0
Wasserstein total variation filtering—0
Winning the ICCV 2019 Learning to Drive Challenge—0
Study of the impact of climate change on precipitation in Paris area using method based on iterative multiscale dynamic time warping (IMS-DTW)—0
Order patterns, their variation and change points in financial time series and Brownian motion—0
You May Not Need Order in Time Series Forecasting—0
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems—0
Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community—0
Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis—0
Generalised learning of time-series: Ornstein-Uhlenbeck 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