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 1151–1175 of 6748 papers

TitleStatusHype
Autoencoder-based time series clustering with energy applications—0
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution—0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network—0
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems—0
AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version—0
An Empirical Evaluation of Similarity Measures for Time Series Classification—0
AutoAI-TS: AutoAI for Time Series Forecasting—0
Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time Series—0
Advancing multivariate time series similarity assessment: an integrated computational approach—0
A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series—0
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
A user-centric model of voting intention from Social Media—0
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings—0
A Unified SVM Framework for Signal Estimation—0
A Unified Method for First and Third Person Action Recognition—0
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems—0
A unified machine learning approach to time series forecasting applied to demand at emergency departments—0
A unified framework of epidemic spreading prediction by empirical mode decomposition based ensemble learning techniques—0
A comparison among some Hurst exponent approaches to predict nascent bubbles in 500 company stocks—0
A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series—0
Augmenting transferred representations for stock classification—0
Advanced Customer Activity Prediction based on Deep Hierarchic Encoder-Decoders—0
Forecasting Weakly Correlated Time Series in Tasks of Electronic Commerce—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection—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