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 2701–2750 of 6748 papers

TitleStatusHype
Comfort Foods and Community Connectedness: Investigating Diet Change during COVID-19 Using YouTube Videos on Twitter—0
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention—0
Folded Graph Signals: Sensing with Unlimited Dynamic Range—0
Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study—0
For2For: Learning to forecast from forecasts—0
Forecastable Component Analysis (ForeCA)—0
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection—0
Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices—0
Comparative analysis of criteria for filtering time series of word usage frequencies—0
A reproduction rate which perfectly fits Covid-19—0
Forecasting and Analyzing the Military Expenditure of India Using Box-Jenkins ARIMA Model—0
Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak—0
Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach—0
Forecasting, capturing and activation of carbon-dioxide (CO_2): Integration of Time Series Analysis, Machine Learning, and Material Design—0
Causal Inference via Kernel Deviance Measures—0
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts—0
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts—0
Forecasting COVID- 19 cases using Statistical Models and Ontology-based Semantic Modelling: A real time data analytics approach—0
Comparative Study of Machine Learning Models for Stock Price Prediction—0
Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning—0
Forecasting Crude Oil Price Using Event Extraction—0
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM—0
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds—0
Forecasting Emerging Trends from Scientific Literature—0
Forecasting euro area inflation using a huge panel of survey expectations—0
Forecasting Exchange Rates Using Time Series Analysis: The sample of the currency of Kazakhstan—0
Forecasting Financial Extremes: A Network Degree Measure of Super-exponential Growth—0
Forecasting financial markets with semantic network analysis in the COVID-19 crisis—0
A Novel Markov Model for Near-Term Railway Delay Prediction—0
Forecasting foreign exchange rates with regression networks tuned by Bayesian optimization—0
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data—0
Forecasting Granular Audience Size for Online Advertising—0
Forecasting Graph Signals with Recursive MIMO Graph Filters—0
Forecasting high-dimensional dynamics exploiting suboptimal embeddings—0
Forecasting high-frequency financial time series: an adaptive learning approach with the order book data—0
Forecasting in multivariate irregularly sampled time series with missing values—0
Causal Inference from Slowly Varying Nonstationary Processes—0
Forecasting intracranial hypertension using multi-scale waveform metrics—0
Enhancing Energy System Models Using Better Load Forecasts—0
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!—0
Forecasting Method for Grouped Time Series with the Use of k-Means Algorithm—0
Forecasting Models for Daily Natural Gas Consumption Considering Periodic Variations and Demand Segregation—0
Log-PDE Methods for Rough Signature Kernels—0
Forecasting Multilinear Data via Transform-Based Tensor Autoregression—0
Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy—0
Forecasting NIFTY 50 benchmark Index using Seasonal ARIMA time series models—0
Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF)—0
Forecasting of a Hierarchical Functional Time Series on Example of Macromodel for Day and Night Air Pollution in Silesia Region: A Critical Overview—0
Forecasting of Non-Stationary Sales Time Series Using Deep Learning—0
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning—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