Data Science - Layering

Weather providers often use historical actuals rather than forecasts. This means different data manipulation, which can be tricky to deal with. At missed we have a way of making this easier by using layering. We store all time series with two time axies: natural and asof time. Our back tests are then made 'as of time aware' so that each job is only trained on the data preceding it.