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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

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Showing 2051–2100 of 6748 papers

TitleStatusHype
EEG to fMRI Synthesis: Is Deep Learning a candidate?—0
Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study—0
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images—0
Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts—0
Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices—0
A Review of Changepoint Detection Models—0
Adaptive Visibility Graph Neural Network and It's Application in Modulation Classification—0
Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data—0
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction—0
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering—0
Comparing Lexical Usage in Political Discourse across Diachronic Corpora—0
Comparative Study of Machine Learning Models for Stock Price Prediction—0
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition—0
Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models—0
EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks—0
Comparative Computational Analysis of Global Structure in Canonical, Non-Canonical and Non-Literary Texts—0
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach—0
Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak—0
A restricted eigenvalue condition for unit-root non-stationary data—0
Adaptive Transmit Waveform Design using Multi-Tone Sinusoidal Frequency Modulation—0
Comparative analysis of criteria for filtering time series of word usage frequencies—0
Compact representation of temporal processes in echosounder time series via matrix decomposition—0
A reproduction rate which perfectly fits Covid-19—0
Co-Morbidity Exploration on Wearables Activity Data Using Unsupervised Pre-training and Multi-Task Learning—0
Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A Wavelet Approach—0
Are Large Language Models Useful for Time Series Data Analysis?—0
A Machine Learning Smartphone-based Sensing for Driver Behavior Classification—0
Adaptive Transfer Learning of Multi-View Time Series Classification—0
Comfort Foods and Community Connectedness: Investigating Diet Change during COVID-19 Using YouTube Videos on Twitter—0
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach—0
Combining Sentinel-1 and Sentinel-2 Time Series via RNN for object-based land cover classification—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers—0
A regression model with a hidden logistic process for feature extraction from time series—0
A machine learning search for optimal GARCH parameters—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers—0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
Combining observational and experimental data to find heterogeneous treatment effects—0
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems—0
EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia—0
Effective Bayesian Modeling of Groups of Related Count Time Series—0
Efficiency of the Moscow Stock Exchange before 2022—0
Combining Generative and Discriminative Neural Networks for Sleep Stages Classification—0
Combining Ensemble Kalman Filter and Reservoir Computing to predict spatio-temporal chaotic systems from imperfect observations and models—0
Are Chinese transport policies effective? A new perspective from direct pollution rebound effect, and empirical evidence from road transport sector—0
Combining Embeddings and Fuzzy Time Series for High-Dimensional Time Series Forecasting in Internet of Energy Applications—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Architectural Tricks for Deep Learning in Remote Photoplethysmography—0
Adaptive Sequential Design for a Single Time-Series—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