Add to ORKGForecasting large numbers of time series is a costly and time-consuming exercise. Before forecasting a large number of series that are logically connected in some way, we can first cluster them into groups of similar series. In this paper we investigate forecasting the series in each cluster. Similar series are first grouped together using a clustering procedure that is based on a test of hypothesis. The series in each cluster are then pooled together and forecasts are obtained. Simulated results show that this procedure for forecasting similar series performs reasonably well
Research on forecasting has traditionally focused on building more accurate statistical models for a...
The current work is devoted to the problem of time series analysis. One of the relevant tasks connec...
Abstract. Predictive clustering is a general framework that unifies clustering and prediction. This ...
Forecasting large numbers of time series is a costly and time-consuming exercise. Before forecasting...
This project presents a new approach to forecast the behavior of time series based on similarity of ...
Linear and nonlinear models for time series analysis and prediction are well-established. Clustering...
This paper proposes a novel time series forecasting method based on a weighted self-constructing clu...
Time series is one of the forms of data presentation that is used in many studies. It is convenient,...
AbstractThis paper presents a new approach to forecast the behavior of time series based on similari...
Demand for forecasting has increased significantly due to the rapid changes in technology, social ch...
Clustering is an essential branch of data mining and statistical analysis that could help us explore...
Time series prediction plays a pivotal role in various areas, including for example finance, weather...
Clustering methods are commonly applied to time series, either as a preprocessing stage for other me...
In this work we consider the problem of clustering time series. Contrary to other works on this topi...
Organizations that use time series forecasting on a regular basis generally forecast many variables,...
Research on forecasting has traditionally focused on building more accurate statistical models for a...
The current work is devoted to the problem of time series analysis. One of the relevant tasks connec...
Abstract. Predictive clustering is a general framework that unifies clustering and prediction. This ...
Forecasting large numbers of time series is a costly and time-consuming exercise. Before forecasting...
This project presents a new approach to forecast the behavior of time series based on similarity of ...
Linear and nonlinear models for time series analysis and prediction are well-established. Clustering...
This paper proposes a novel time series forecasting method based on a weighted self-constructing clu...
Time series is one of the forms of data presentation that is used in many studies. It is convenient,...
AbstractThis paper presents a new approach to forecast the behavior of time series based on similari...
Demand for forecasting has increased significantly due to the rapid changes in technology, social ch...
Clustering is an essential branch of data mining and statistical analysis that could help us explore...
Time series prediction plays a pivotal role in various areas, including for example finance, weather...
Clustering methods are commonly applied to time series, either as a preprocessing stage for other me...
In this work we consider the problem of clustering time series. Contrary to other works on this topi...
Organizations that use time series forecasting on a regular basis generally forecast many variables,...
Research on forecasting has traditionally focused on building more accurate statistical models for a...
The current work is devoted to the problem of time series analysis. One of the relevant tasks connec...
Abstract. Predictive clustering is a general framework that unifies clustering and prediction. This ...