Error Correction methodologies Time Series Forecast

Do you have any readings recommendation on correcting forecast bias? For example, I use an ARIMA model to predict a time series. Is there a way based on the backtesting results to correct the bias of the forecast?

How to handle an all present Bias / Overfit struggle?

Using a tactical methodology:

one principal approach to this is to systematically tune a Predictor ( be it ARIMA or some other ) via a two step approach.

You have to split available DataSET into two parts, so as to emulate a near "Future", and "hide" the -- say about 20-30% of the observations -- second part of the DataSET from a process of [1] Training and find it's use in a step [2] called CrossValidation of predictions.

This methodology allows one to search both the StateSPACE of a Predictor engine's configurations and data-related bias/overfit. Some use only the former part of the minimiser search ( lowest error / highest utility function ), some only the latter ( alike Leo Breiman's RandomForest modification of ensemble based method ) and some use both.

1. Train a pre-configured Predictor on aTrainingSubPartOfAvailableDataSET
2. Once such a configuration of a Predictor got trained, cross-validate this configuration's ability to predict against aCrossValidationSubPartOfAvailableDataSET not seen in the process of training (Step 1.) to observe the Bias / Overfit artefacts and proceed towards the lowest Cross-Validation error / best generalisation area of plausible configuration settings.