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

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Showing 4151–4200 of 6748 papers

TitleStatusHype
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks—0
k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)Code1
Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)—0
Flexible Transmitter Network—0
Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs—0
Probabilistic Spatial Transformer NetworksCode0
Challenges in Forecasting Malicious Events from Incomplete Data—0
TSInsight: A local-global attribution framework for interpretability in time-series data—0
Forecasting in multivariate irregularly sampled time series with missing values—0
Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov ModelCode1
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series—0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—0
Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack—0
ForecastTB An R Package as a Test-Bench for Time Series Forecasting Application of Wind Speed and Solar Radiation Modeling—0
Spatio-Temporal Graph Structure Learning for Traffic Forecasting—0
Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units—0
Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder—0
Enhance the performance of navigation: A two-stage machine learning approach—0
Time-varying volatility in Bitcoin market and information flow at minute-level frequency—0
Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach—0
Stationarity of Time-Series on Graph via Bivariate Translation Invariance—0
From Fourier to Koopman: Spectral Methods for Long-term Time Series PredictionCode1
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art—0
COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios—0
When Ramanujan meets time-frequency analysis in complicated time series analysis—0
Current state of nonlinear-type time-frequency analysis and applications to high-frequency biomedical signals—0
A wavelet analysis of inter-dependence, contagion and long memory among global equity markets—0
The fractal time growth of COVID-19 pandemic: an accurate self-similar model, and urgent conclusions—0
Financial Market Trend Forecasting and Performance Analysis Using LSTM—0
Machine Learning Algorithms for Financial Asset Price Forecasting—0
Adversarial Attacks on Multivariate Time Series—0
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic PredictionCode1
Pruned Wasserstein Index Generation Model and wigpy PackageCode0
Half-empty or half-full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime—0
Difference Attention Based Error Correction LSTM Model for Time Series Prediction—0
High-dimensional mixed-frequency IV regression—0
Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation modelCode0
Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods—0
A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting—0
Topological Data Analysis in Text Classification: Extracting Features with Additive Information—0
Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment RecognitionCode0
Circulant Singular Spectrum Analysis: A new automated procedure for signal extraction—0
Correlated daily time series and forecasting in the M4 competitionCode0
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data—0
ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series—0
Spatiotemporal Adaptive Neural Network for Long-term Forecasting of Financial Time Series—0
Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis—0
Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks—0
Triad State Space Construction for Chaotic Signal Classification with Deep Learning—0
FastDTW is approximate and Generally Slower than the Algorithm it ApproximatesCode1
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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