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
A Review of Intelligent Practices for Irrigation Prediction0
Deep Symbolic Representation Learning for Heterogeneous Time-series Classification0
Evaluating the Performance of ANN Prediction System at Shanghai Stock Market in the Period 21-Sep-2016 to 11-Oct-20160
Intra-day Activity Better Predicts Chronic Conditions0
Positive blood culture detection in time series data using a BiLSTM network0
Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework0
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy0
Predicting Patient State-of-Health using Sliding Window and Recurrent Classifiers0
Fast Active Set Methods for Online Spike Inference from Calcium Imaging0
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction0
Graphical Time Warping for Joint Alignment of Multiple Curves0
Probabilistic Broken-Stick Model: A Regression Algorithm for Irregularly Sampled Data with Application to eGFR0
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models0
Complex-valued Gaussian Process Regression for Time Series Analysis0
SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data0
Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time Series0
Exploring Strategies for Classification of External Stimuli Using Statistical Features of the Plant Electrical Response0
Times series averaging and denoising from a probabilistic perspective on time-elastic kernelsCode0
Split-door criterion: Identification of causal effects through auxiliary outcomesCode0
Person Re-Identification by Unsupervised Video Matching0
Multiple Time Series Ising Model for Financial Market Simulations0
Probabilistic structure discovery in time series data0
Time Series Structure Discovery via Probabilistic Program Synthesis0
Time Series Classification from Scratch with Deep Neural Networks: A Strong BaselineCode3
Evaluating genetic drift in time-series evolutionary analysis0
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models0
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets0
Earliness-Aware Deep Convolutional Networks for Early Time Series Classification0
Low Latency Anomaly Detection and Bayesian Network Prediction of Anomaly Likelihood0
Measurement of Anticipative Power of a Retina by Predictive Information0
Why is it Difficult to Detect Sudden and Unexpected Epidemic Outbreaks in Twitter?0
Combining observational and experimental data to find heterogeneous treatment effects0
NonSTOP: A NonSTationary Online Prediction Method for Time Series0
Learning Time Series Detection Models from Temporally Imprecise Labels0
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data ProcessingCode1
Causal Compression0
The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes0
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks0
All-atom Molecular Dynamics Simulations of the Projection Domain of the Intrinsically Disordered htau40 Protein0
Limits to causal inference with state-space reconstruction for infectious disease0
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories0
Tool and Phase recognition using contextual CNN features0
Recurrent switching linear dynamical systemsCode0
Universality of Bayesian mixture predictors0
Are Chinese transport policies effective? A new perspective from direct pollution rebound effect, and empirical evidence from road transport sector0
Gaussian Process Kernels for Popular State-Space Time Series Models0
Distributed and parallel time series feature extraction for industrial big data applicationsCode0
SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time SeriesCode0
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition0
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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