Recent Blogs Posts

In the previous articles, we discussed the FEDformer method that uses the frequency domain to find patterns in a time series. However, the Transformer used in that method can hardly be referred to as a lightweight model. Instead of complex models that require large computational costs, the paper "FITS: Modeling Time Series with 10k Parameters" proposes a method for the frequency interpolation of time series (Frequency Interpolation Time Series - FITS). It is a compact and efficient solution for

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A common approach when processing time series is to keep the original arrangement of the time steps intact. It is assumed that the historical order is the most optimal. However, most existing models lack explicit mechanisms to explore the relationships between distant segments within each time series, which may in fact have strong dependencies. For example, models based on convolutional networks (CNN) used for time series learning can only capture patterns within a limited time window. As a result,

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