The aim was to compare and classify these literatures and put forward reference for scholars who research development, new techniques and trends of time series data mining.doi:10.3969/j.issn.1001-3695.2007.11.004JIA PengtaoHE HuacanLIU Li
Therefore, an effective mechanism for compressing the huge amount of time series data, especially historical data, is needed. This not only reduces the size of storage, but also maintains an acceptable level of information for the discovery process. Fu et al. (2001) propose to adopt PIP ...
In the past, this task was performed by teams of data engineers and data scientists. Today, however, much of data processing is done by AI and machine learning (ML) algorithms. While the nature of processing indicates at least some kind of time delay, the speed or lack of "heavy" proces...
An overview of the Space Time Pattern Mining toolbox Space Time Pattern Mining toolbox licensing Space Time Pattern Mining toolbox history Space Time Cube Creation toolset Space Time Cube Visualization toolset Space Time Pattern Analysis toolset Time Series Forecasting tool...
1. INTRODUCTION A spatial trajectory is a trace generated by a moving object in geographical spaces, usually represented by a series of chronologically ordered points, e. g. 1 → 2 → ⋯ → , where each point consists of a geospatial coordinate set and a timestamp such as = ( , , )...
The Space Time Pattern Mining toolbox contains statistical tools for analyzing data distributions and patterns in the context of both space and time. It includes a toolset that can be helpful for visualizing the data stored in the space-time netCDF cube in both 2D and 3D and filling missing ...
These methods are gaining popularity as a result of traditional statistical methods not scaling up to the needs of analyzing large databases, as well as for reasons of user-friendliness, i.e., the need for the analysis to be performed by the end-users themselves, often in real time....
A Time Series Model is defined as a method used in various scientific fields to analyze data exhibiting patterns like trends, seasonal fluctuations, and irregular cycles. It involves building linear models, such as ARMA and ARIMA models, to forecast future observations, estimate the impact of inter...
Multivariate Time Series refers to a type of data that consists of multiple variables recorded over time, where each variable can have different sampling frequencies, varying numbers of measurements, and different periodicities. It is commonly used in various fields such as industrial automation, health...
Fig.1according to Google Trends data over the last 5 years [36]. In addition to data science, we have also shown the popularity trends of the relevant areas such as “Data analytics”, “Data mining”, “Big data”, “Machine learning” in the figure. According to Fig.1, the popularit...