This is how I think about this: In order to forecast something we need certain amount of information, in our case the information is within the heatmaps. I assume the **link strength** is also obtained from the same heatmaps, therefore no new amount of information is added to the forecast, therefore generally speaking, not just for a few of the sample cases, then any heatmap related information could not aid the forecast.

However, non-heatmap related e.g. upper atmosphere radiation quantities or something completely unrelated then adds to the amount of information, possibly allow for better forecast.

Example1: If I am riding my bike the past history of riding a bike could enable me to balance and follow a path, however at the next bent in the road there is a wall and a large occluded hole beside it. No matter what data I add from the history of the bike ride will not allow me to forecast a navigation to circumvent the hole, but if someone shouted "Watch out hole behind the wall" , that information could easily help to modify the forecast.

Example2: I write forecast algorithms for stock's price, then if I use the same stock price history and make up a new variable might not greatly improve my forecast since I added no new information. So what is proposed nowadays is to add data from text of the company report releases e.g. 10k filings, then I could add a new variable to the input vector with added information which could increase the forecast accuracy.