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 6426–6450 of 6748 papers

TitleStatusHype
Discovering Causal Relations in Textual Instructions—0
Squibs: Evaluation Methods for Statistically Dependent Text—0
Forecasting Exchange Rates Using Time Series Analysis: The sample of the currency of Kazakhstan—0
Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm—0
Duration and Interval Hidden Markov Model for Sequential Data Analysis—0
The (in)visible hand in the Libor market: an Information Theory approach—0
Time Series Clustering via Community Detection in NetworksCode0
Fuzzy Longest Common Subsequence Matching With FCM Using R—0
Bridging AIC and BIC: a new criterion for autoregression—0
Time-series modeling with undecimated fully convolutional neural networks—0
Quantitative evaluation of the performance of discrete-time reservoir computers in the forecasting, filtering, and reconstruction of stochastic stationary signals—0
Forecasting Leading Death Causes in Australia using Extended CreditRisk+—0
Modeling Website Workload Using Neural Networks—0
Evaluation of Spectral Learning for the Identification of Hidden Markov Models—0
Chaotic Neuronal Oscillations in Spontaneous Cortical-Subcortical Networks—0
Multi-scaling of wholesale electricity prices—0
AMP: a new time-frequency feature extraction method for intermittent time-series data—0
Quantile Correlations: Uncovering temporal dependencies in financial time series—0
Recursive Sparse Point Process Regression with Application to Spectrotemporal Receptive Field Plasticity Analysis—0
Semi-parametric time series modelling with autocopulas—0
Ensemble of Hankel Matrices for Face Emotion Recognition—0
Quantum Gates and Quantum Circuits of Stock Portfolio—0
Learning Leading Indicators for Time Series Predictions—0
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes—0
A Frame of Mind: Using Statistical Models for Detection of Framing and Agenda Setting Campaigns—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