FinanceFeeds

Intro to On-Chain Time-Series Data & Why It Matters

When this data is organized chronologically—tracking changes in a specific metric over time—it becomes time-series data.
IEEE

Feature Weighting-Based Deep Fuzzy C-Means for Clustering Incomplete Time Series

Abstract: Time-series clustering is a crucial unsupervised technique for analyzing data, commonly used in various fields, including medicine and stock analysis. However, in real-world scenarios, ...
Scientific Research Publishing

Paparrizos, J., Morvant, E., & Theodoridis, S. (2024). Bridging the Gap: A Decade Review of Time-Series Clustering Methods.

ABSTRACT: Stock returns exhibit nonlinear dynamics and volatility clustering. It is well known that we cannot forecast the movements of stock prices under the condition that market is efficient. In ...
GitHub

Awesome Time Series Papers

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Code ICML 2025 TimeFilter: Patch‑Specific Spatial‑Temporal Graph Filtration for Time Series Forecasting Code ICML 2025 K² ...
Scientific Research Publishing

On Prediction of Stock Return and Volatility Using Clustering Techniques: Taking an Example of Japanese Stock Market ()

Stock returns exhibit nonlinear dynamics and volatility clustering. It is well known that we cannot forecast the movements of stock prices under the condition that market is efficient. In most ...
qualitysafety.bmj

Cluster randomised evaluation of a training intervention to increase the use of statistical process control charts for hospitals in England: making data count

Background The way that data are presented can influence quality and safety initiatives. Time-series charts highlight changes but do not clarify whether data lie outside expected variation.
IEEE

Time-Series Clustering With Dynamic Feature Mining in the Framework of Belief Function Theory

Abstract: Clustering time-series data has gained abundant popularity and has been widely used in diverse scientific areas. However, few studies have systematically addressed the ambiguity and ...
Microsoft

TimeDP: Creating cross-domain synthetic time-series data

Time-series data—measurements collected over time like stock prices or heart rates—plays a vital role in AI forecasting systems across industries. As these systems advance, the need for time-series ...
Hacker

Implementing Clustering Methods to Rearrange Time Series Data

In this study, our focus is on implementing clustering methods to rearrange time series data that represent technological proximity based on their shapes across various index types. The objective of ...
GitHub

Cluster Analysis at Duke University for Median Graduation Time Across Doctorate Programs

Doctorate programs are large time investments, and for students who are trying to decide between different life science programs of study, time until graduation may be an important deciding factor.
news.crunchbase

Startups Take Longer To Close Rounds, As Funding Cliff Looms

The time lapse between funding rounds for early-stage startups has reached the highest point in more than a decade. That was the finding from a Crunchbase survey of U.S. companies that raised Series A ...