Three Ways to Look at Time — Part 7: ST-ResNet

ST-ResNet's core insight is that not all history is created equal. When you're predicting crime in Auckland next month, three different kinds of past information matter. What happened in the last couple of months — the recent trend. What happened at the same time last year — the seasonal pattern. And what's been happening over the longer term — whether crime is generally rising or falling in an area. ConvLSTM treats all of this as one continuous sequence and hopes the network figures out which parts matter. ST-ResNet takes a more opinionated approach — it separates these three temporal scales explicitly and gives each one its own dedicated neural network branch. The original paper by Zhang et al. was about predicting crowd flows in Beijing. People move through cities in patterns that look a lot like crime patterns — daily rhythms, weekly cycles, long-term trends. The architecture translates well to crime data, with some modifications. Closeness, period, trend The three branches each…