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 61016150 of 6748 papers

TitleStatusHype
Deep State Inference: Toward Behavioral Model Inference of Black-box Software SystemsCode0
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time SeriesCode0
Bayesian Spillover Graphs for Dynamic NetworksCode0
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient EncoderCode0
A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant informationCode0
Learning Sequential Latent Variable Models from Multimodal Time Series DataCode0
Generative Adversarial Network for Future Hand Segmentation from Egocentric VideoCode0
Balanced Graph Structure Learning for Multivariate Time Series ForecastingCode0
Stock Movement Prediction from Tweets and Historical PricesCode0
Two-Sample Testing for Event Impacts in Time SeriesCode0
Tight lower bounds for Dynamic Time WarpingCode0
Generating Sparse Counterfactual Explanations For Multivariate Time SeriesCode0
A Statistical Investigation of Long Memory in Language and MusicCode0
Stock Price Prediction Based on Natural Language ProcessingCode0
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend FilteringCode0
Learning the Dynamics of Sparsely Observed Interacting SystemsCode0
Generating Reliable Process Event Streams and Time Series Data based on Neural NetworksCode0
Convolutional Reservoir Computing for World ModelsCode0
Dependent Latent Class ModelsCode0
Asset Price Forecasting using Recurrent Neural NetworksCode0
Bayesian Online Changepoint DetectionCode0
Convolutional Conditional Neural ProcessesCode0
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open QuestionsCode0
Bayesian Nonparametric Spectral EstimationCode0
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting SystemCode0
Continuous-time convolutions model of event sequencesCode0
Reduced-order modeling with artificial neurons for gravitational-wave inferenceCode0
Timage -- A Robust Time Series Classification PipelineCode0
Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time SeriesCode0
"Back to the future" projections for COVID-19 surgesCode0
Continuous Meta-Learning without TasksCode0
Learning to Detect Sepsis with a Multitask Gaussian Process RNN ClassifierCode0
Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with LoopsCode0
Time2Graph: Revisiting Time Series Modeling with Dynamic ShapeletsCode0
Learning to Forecast Dynamical Systems from Streaming DataCode0
A Data Cube of Big Satellite Image Time-Series for Agriculture MonitoringCode0
Option Pricing and Hedging for Discrete Time Autoregressive Hidden Markov ModelCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
backShift: Learning causal cyclic graphs from unknown shift interventionsCode0
Learning towards Abstractive Timeline SummarizationCode0
Learning orbital dynamics of binary black hole systems from gravitational wave measurementsCode0
Learning Video-independent Eye Contact Segmentation from In-the-Wild VideosCode0
ORBITS: Online Recovery of Missing Blocks in Multiple Time Series StreamsCode0
Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell CultivationCode0
Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using TwitterCode0
General anesthesia reduces complexity and temporal asymmetry of the informational structures derived from neural recordings in DrosophilaCode0
TimeCaps: Capturing Time Series Data With Capsule NetworksCode0
Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent UnitsCode0
Assessing Differentially Private Variational Autoencoders under Membership InferenceCode0
Towards an Awareness of Time Series Anomaly Detection Models' Adversarial VulnerabilityCode0
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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