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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 51–75 of 6748 papers

TitleStatusHype
Decoding Financial Health in Kenyas' Medical Insurance Sector: A Data-Driven Cluster Analysis—0
ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series ForecastingCode1
Demand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysis—0
Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generation—0
An LLM-Powered Agent for Physiological Data Analysis: A Case Study on PPG-based Heart Rate Estimation—0
Positional Encoding in Transformer-Based Time Series Models: A SurveyCode1
Noumenal Labs White Paper: How To Build A Brain—0
Comprehensive Review of Neural Differential Equations for Time Series Analysis—0
Harnessing Vision Models for Time Series Analysis: A SurveyCode2
SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating MechanismsCode0
Time Series Analysis of Rankings: A GARCH-Type Approach—0
General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data—0
LAST SToP For Modeling Asynchronous Time Series—0
Position: Empowering Time Series Reasoning with Multimodal LLMs—0
Large Language Models are Few-shot Multivariate Time Series Classifiers—0
Enhancing Visual Inspection Capability of Multi-Modal Large Language Models on Medical Time Series with Supportive Conformalized and Interpretable Small Specialized ModelsCode0
Unifying Prediction and Explanation in Time-Series Transformers via Shapley-based Pretraining—0
Time Series Embedding Methods for Classification Tasks: A ReviewCode1
An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial dataCode0
DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing—0
Logarithmic Memory Networks (LMNs): Efficient Long-Range Sequence Modeling for Resource-Constrained EnvironmentsCode0
The Utility of Hyperplane Angle Metric in Detecting Financial Concept DriftCode0
BRATI: Bidirectional Recurrent Attention for Time-Series Imputation—0
Behavioural Analytics: Mathematics of the Mind—0
Time Series Language Model for Descriptive Caption Generation—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