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 6201–6250 of 6748 papers

TitleStatusHype
Temporal Registration in In-Utero Volumetric MRI Time Series—0
Dynamic structure of stock communities: A comparative study between stock returns and turnover rates—0
Dynamic Principal Component Analysis: Identifying the Relationship between Multiple Air Pollutants—0
Revisiting Causality Inference in Memory-less Transition Networks—0
Signs in time: Encoding human motion as a temporal image—0
A Distance for HMMs based on Aggregated Wasserstein Metric and State RegistrationCode0
Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms—0
Fast and Simple Optimization for Poisson Likelihood Models—0
Analyzing Linear Dynamical Systems: From Modeling to Coding and LearningCode0
Size-Consistent Statistics for Anomaly Detection in Dynamic Networks—0
Towards a text analysis system for political debates—0
Towards Building a Political Protest Database to Explain Changes in the Welfare State—0
Exploring Long-Term Temporal Trends in the Use of Multiword Expressions—0
Mining linguistic tone patterns with symbolic representation—0
Text authorship identified using the dynamics of word co-occurrence networks—0
Wavelet algorithm for the identification of P300 ERP component—0
Dynamic Probabilistic Network Based Human Action Recognition—0
Effective Connectivity-Based Neural Decoding: A Causal Interaction-Driven Approach—0
Distributed Supervised Learning using Neural Networks—0
A Comparison of Nineteen Various Electricity Consumption Forecasting Approaches and Practicing to Five Different Households in Turkey—0
Identification of market trends with string and D2-brane maps—0
Hierarchical Multi-resolution Mesh Networks for Brain Decoding—0
A conditional likelihood is required to estimate the selection coefficient in ancient DNA—0
ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data—0
Real-Time Anomaly Detection for Streaming AnalyticsCode0
Automatic Generation of Probabilistic Programming from Time Series Data—0
Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets—0
Efficient and Consistent Robust Time Series Analysis—0
LSTM-based Encoder-Decoder for Multi-sensor Anomaly DetectionCode2
Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms—0
Disease Trajectory Maps—0
Replica approach to mean-variance portfolio optimization—0
Facial Expression Classification Using Rotation Slepian-based Moment Invariants—0
Among-site variability in the stochastic dynamics of East African coral reefs—0
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series—0
A Novel Algorithm for the Maximal Fit Problem in Boolean Networks—0
Adapting ELM to Time Series Classification: A Novel Diversified Top-k Shapelets Extraction Method—0
Complex systems: features, similarity and connectivity—0
PSF : Introduction to R Package for Pattern Sequence Based Forecasting Algorithm—0
Deep Learning for MusicCode0
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classificationCode0
Modeling Missing Data in Clinical Time Series with RNNs—0
metricDTW: local distance metric learning in Dynamic Time Warping—0
Kolmogorov Space in Time Series Data—0
Specific Differential Entropy Rate Estimation for Continuous-Valued Time SeriesCode0
shapeDTW: shape Dynamic Time WarpingCode0
Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach—0
Recurrent Neural Networks for Multivariate Time Series with Missing ValuesCode1
Forecasting Framework for Open Access Time Series in Energy—0
Deep Canonical Time Warping—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