  Time series prediction is a prevalent issue across various disciplines, such
as meteorology, traffic surveillance, investment, and energy production and
consumption. Many statistical and machine-learning strategies have been
developed to tackle this problem. However, these approaches either lack
explainability or exhibit less satisfactory performance when the prediction
horizon increases. To this end, we propose a novel plan for the designing of
networks' architecture based on transformations, possessing the potential to
achieve an enhanced receptive field in learning which brings benefits to fuse
features across scales. In this context, we introduce four different
transformation mechanisms as bases to construct the learning model including
Fourier Transform (FT), Singular Value Decomposition (SVD), matrix
multiplication and Conv block. Hence, we develop four learning models based on
the above building blocks, namely, FT-Matrix, FT-SVD, FT-Conv, and Conv-SVD.
Note that the FT and SVD blocks are capable of learning global information,
while the Conv blocks focus on learning local information. The matrix block is
sparsely designed to learn both global and local information simultaneously.
The above Transformation Learning Networks (TLNets) have been extensively
tested and compared with multiple baseline models based on several real-world
datasets and showed clear potential in long-range time-series forecasting.
