With the recent first light milestone for the Vera Rubin Observatory, it's only a matter of time before one of astronomy's ...

Multivariate Time Series (MTS) forecasting is to accurately predict future trends through in-depth analysis of historical time series data, and to provide valuable reference to support for ...

After preparing the data, it was necessary to pay attention to some points to use the time series method to implement the Interrupted Time Series Method. One of these points was to draw graphs with ...

Graph Neural Network (GNN) can overcome these challenges by capturing the temporal dependencies in time-series data effectively. In this study, we propose a novel approach for predicting time-series ...

At present, this model has been proven to be superior in graph data structure and time series information analysis in many fields, including traffic flow prediction (Zhao et al., 2020). However, this ...

TigerGraph, a well-funded enterprise startup that provides a graph database and analytics platform, today announced that it has raised a $105 million Series C funding round. The round was led by ...

Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.

Time series databases provide optimum support for working with time-dependent data. This post is one of a series that introduces the fundamentals of NOSQL databases, and their role in Big Data ...