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 6701–6748 of 6748 papers

TitleStatusHype
FIXED: Frustratingly Easy Domain Generalization with Mixup—0
FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data—0
Flexible conditional density estimation for time series—0
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series—0
Flexible Transmitter Network—0
Flow Forecast: A deep learning for time series forecasting, classification, and anomaly detection framework built in PyTorch—0
fMRI-Kernel Regression: A Kernel-based Method for Pointwise Statistical Analysis of rs-fMRI for Population Studies—0
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention—0
Folded Graph Signals: Sensing with Unlimited Dynamic Range—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
Forecasting and Analyzing the Military Expenditure of India Using Box-Jenkins ARIMA Model—0
Forecasting, capturing and activation of carbon-dioxide (CO_2): Integration of Time Series Analysis, Machine Learning, and Material Design—0
Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media—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
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
Forecasting foreign exchange rates with regression networks tuned by Bayesian optimization—0
Forecasting Framework for Open Access Time Series in Energy—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
Forecasting intracranial hypertension using multi-scale waveform metrics—0
Forecasting Leading Death Causes in Australia using Extended CreditRisk+—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
Forecasting Multi-Dimensional Processes over Graphs—0
Forecasting Multilinear Data via Transform-Based Tensor Autoregression—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
Forecasting Photovoltaic Power Production using a Deep Learning Sequence to Sequence Model with Attention—0
Forecasting Player Behavioral Data and Simulating in-Game Events—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