  Time series classification is one of the most critical and challenging
problems in data mining, existing widely in various fields and holding
significant research importance. Despite extensive research and notable
achievements with successful real-world applications, addressing the challenge
of capturing the appropriate receptive field (RF) size from one-dimensional or
multi-dimensional time series of varying lengths remains a persistent issue,
which greatly impacts performance and varies considerably across different
datasets. In this paper, we propose an Adaptive and Effective Full-Scope
Convolutional Neural Network (AdaFSNet) to enhance the accuracy of time series
classification. This network includes two Dense Blocks. Particularly, it can
dynamically choose a range of kernel sizes that effectively encompass the
optimal RF size for various datasets by incorporating multiple prime numbers
corresponding to the time series length. We also design a TargetDrop block,
which can reduce redundancy while extracting a more effective RF. To assess the
effectiveness of the AdaFSNet network, comprehensive experiments were conducted
using the UCR and UEA datasets, which include one-dimensional and
multi-dimensional time series data, respectively. Our model surpassed baseline
models in terms of classification accuracy, underscoring the AdaFSNet network's
efficiency and effectiveness in handling time series classification tasks.
