  This paper introduces ConvTimeNet, a novel deep hierarchical fully
convolutional network designed to serve as a general-purpose model for time
series analysis. The key design of this network is twofold, designed to
overcome the limitations of traditional convolutional networks. Firstly, we
propose an adaptive segmentation of time series into sub-series level patches,
treating these as fundamental modeling units. This setting avoids the sparsity
semantics associated with raw point-level time steps. Secondly, we design a
fully convolutional block by skillfully integrating deepwise and pointwise
convolution operations, following the advanced building block style employed in
Transformer encoders. This backbone network allows for the effective capture of
both global sequence and cross-variable dependence, as it not only incorporates
the advancements of Transformer architecture but also inherits the inherent
properties of convolution. Furthermore, multi-scale representations of given
time series instances can be learned by controlling the kernel size flexibly.
Extensive experiments are conducted on both time series forecasting and
classification tasks. The results consistently outperformed strong baselines in
most situations in terms of effectiveness.The code is publicly available.
