Add to ORKGThis paper presents the CATS Benchmark and the results of the competition organised during the IJCNN’04 conference in Budapest. Twenty-four papers and predictions have been submitted and seventeen have been selected. The goal of the competition was the prediction of 100 missing values divided into five groups of twenty consecutive values
This article reports the results of an exploratory data analysis, as well as the prediction accuracy...
This paper presents the development of an improved Fuzzy Time Series (FTS) forecasting model with Ca...
We calculate different time series characteristics for our data set (libra) and the time series comp...
This paper presents the CATS Benchmark and the results of the competition organised during the IJCNN...
An approach to time series prediction of the CATS benchmark (for competition on artificial time seri...
This paper discusses a solution for the CATS benchmark of time series prediction competition in IJCN...
International audienceThe Double Vector Quantization method, a long-term forecasting method based on...
Abstract — In this paper, time series prediction is considered as a problem of missing values. A met...
The double vector quantization forecasting method based on Kohonen self-organizing maps is applied t...
We use a multi-stream extended Kalman filter for the CATS benchmark (Competition on Artificial Time ...
The M4 forecasting competition challenged the participants to forecast 100,000 time series with diff...
way to contrast the behaviour of different algorithms in the field of time-One forecasting is to com...
This book presents selected peer-reviewed contributions from the International Conference on Time Se...
This book presents selected peer-reviewed contributions from the International Work-Conference on Ti...
This paper presents the development of an improved Fuzzy Time Series (FTS) forecasting model with Ca...
This article reports the results of an exploratory data analysis, as well as the prediction accuracy...
This paper presents the development of an improved Fuzzy Time Series (FTS) forecasting model with Ca...
We calculate different time series characteristics for our data set (libra) and the time series comp...
This paper presents the CATS Benchmark and the results of the competition organised during the IJCNN...
An approach to time series prediction of the CATS benchmark (for competition on artificial time seri...
This paper discusses a solution for the CATS benchmark of time series prediction competition in IJCN...
International audienceThe Double Vector Quantization method, a long-term forecasting method based on...
Abstract — In this paper, time series prediction is considered as a problem of missing values. A met...
The double vector quantization forecasting method based on Kohonen self-organizing maps is applied t...
We use a multi-stream extended Kalman filter for the CATS benchmark (Competition on Artificial Time ...
The M4 forecasting competition challenged the participants to forecast 100,000 time series with diff...
way to contrast the behaviour of different algorithms in the field of time-One forecasting is to com...
This book presents selected peer-reviewed contributions from the International Conference on Time Se...
This book presents selected peer-reviewed contributions from the International Work-Conference on Ti...
This paper presents the development of an improved Fuzzy Time Series (FTS) forecasting model with Ca...
This article reports the results of an exploratory data analysis, as well as the prediction accuracy...
This paper presents the development of an improved Fuzzy Time Series (FTS) forecasting model with Ca...
We calculate different time series characteristics for our data set (libra) and the time series comp...