Avoiding Look Ahead Bias in Time Series Modelling

Any time series classification or regression forecasting involves the Y prediction at't n' given the X and Y information available till time T. Obviously no data scientist or statistician can deploy the system without back testing and validating the performance of model in history. Using the future actual information in training data which could be termed as "Look Ahead Bias" is probably the gravest mistake a data scientist can make. Even the sentence "we cannot make use future data in training" sounds too obvious and simple in theory, anyone unknowingly can add look ahead bias in complex forecasting problems. The discussion becomes important when you put in so much efforts in researching and building the model only to realize later that the back testing framework was using future data. It will also cost the data scientist a lot when the model is approved by Top Management and at the time of deploying the model realizing that we don't have the future data.