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 3201–3225 of 6748 papers

TitleStatusHype
Improved Grey System Models for Predicting Traffic Parameters—0
Improved Modeling of Complex Systems Using Hybrid Physics/Machine Learning/Stochastic Models—0
Emotion-Inspired Deep Structure (EiDS) for EEG Time Series Forecasting—0
Improved PAC-Bayesian Bounds for Linear Regression—0
Improved Prediction and Network Estimation Using the Monotone Single Index Multi-variate Autoregressive Model—0
Improved Predictive Deep Temporal Neural Networks with Trend Filtering—0
Causal Digital Twin from Multi-channel IoT—0
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)—0
Improvement of Flood Extent Representation with Remote Sensing Data and Data Assimilation—0
Curriculum Learning in Deep Neural Networks for Financial Forecasting—0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis—0
Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks—0
Emotional Expression Classification using Time-Series Kernels—0
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems—0
Improving Convolutional Neural Networks for Fault Diagnosis by Assimilating Global Features—0
D2KE: From Distance to Kernel and Embedding—0
Causal Consistency of Structural Equation Models—0
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition—0
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning—0
Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time—0
Causal Compression—0
Improving MF-DFA model with applications in precious metals market—0
Dalek -- a deep-learning emulator for TARDIS—0
Embedding Symbolic Temporal Knowledge into Deep Sequential Models—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