Notes for Completion of Form NN5 . Introduction . 1. If there is a change in the charter, statutes or memorandum (including articles, if any) of a registered non-Hong Kong company or other instruments defining the company’s constitution, the company must,

We seek to evaluate the accuracy of computational intelligence (CI) methods in time series forecasting, extending the earlier NN3 forecasting competition unto a new set of daily data. We hope to evaluate progress in modelling neural networks for forecasting & to disseminate knowledge on “best practices”.

Below, please find the download links for both the reduced and complete competition datasets, as they were handed out to the contestants. In addition, please find the complete data of all time series including the withheld values of the test data (file …

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits 5 …

为了达到这样的目的，我们使用一个大型实验基准（即NN5预测竞赛的111系列）对这些不同的策略进行了大规模的比较。 此外，我们还考虑了去松散化、输入变量选择和预测组合对这些策 …

Form NN5 to report the change in the charter, statutes or memorandum of other instruments defining the constitution of a registered non-Hong Kong company within 1 month after the date of change for registration.

NN5 Competition Results. Results on the Complete Dataset of 111 Time Series . The results will be disclosed in mid 2008.

2023年7月22日 · NN5 data Description. Data from the NN5 forecasting competition, comprising 111 daily time series. Training and test data are combined. In the competition, the last 56 days were used as test data. Time series NN5-101 to NN5-111 made up the reduced data set from the competition. Usage nn5 Format. A list of time series data, each of class msts ...

2012年6月15日 · The specificity of the NN5 series requires a preprocessing step called gaps removal where by gap we mean two types of anomalies: (i) zero values that indicate that no money withdrawal occurred and (ii) missing observations for which no value was recorded. About 2.5% of the data are corrupted by gaps.

2021年1月21日 · The NN5 Competition's Dataset A contains 111 different daily time series. Each of these time series possesses 735 observations, and may present missing data. The time series also show different patterns of single or multiple overlying seasonal properties.