Finding Outliers in Resource Forecast Data
There is not one way of detecting outliers. Most statistical methods assume an underlying normal distribution. If this is the case you could apply for example an Thompson Tau test. You can also define some own criteria. In your use case I think an important question is: what will happen to the quality of the model if you drop or modify missing data. This gives you an idea how to handle these missing data points.