Standard forecaster model

We will need to develop an “off-the-shelf” forecaster model that uses a combination of:

  • network data (e.g. historical inference losses, raw inferences, network inferences, worker scores, worker rewards);
  • private data (critical context specific to the target variable that the workers are providing inferences for, which can be used to characterise under which conditions inferences are expected to be more or less accurate);

to forecast the loss of each raw inference under the current conditions. This model should be as general and problem-agnostic as possible, so that it can be used with minor changes in any Allora topic. As part of the pipeline, we need some logic that merges the network data and private data, which will probably involve some block height ↔ timestamp mapping.

Given that the forecasters are critical to Allora’s context awareness, we should make the model as accurate as possible out of the box. Obviously, workers running forecaster models are expected to differentiate themselves by adding their private data and developing these models further.