Root cause analysis and how to read it
Root cause analysis¶
Root cause analysis can be a valuable source of information provided over your forecasting result. One can trigger its calculation for an already finished forecasting job to see why the forecast looks the way it does and how the Model Zoo is constructed.
Structure of the output¶
datetime  date_from  …  model_index  …  bin  term_1  term_2  term_3  …  term_N  yhat_1  yhat_2  yhat_3  …  yhat_N 

20141028T03:00:00.0  20141028  …  1  …  1  2546  900  943.05  …  624  1943  1984  1987  …  1996 
20141028T04:00:00.0  20141027  …  7  …  2  2451  5000  5409.6  …  2195  2104  2089  …  
20141028T04:00:00.0  20141027  …  6  …  2  2103  200  65363.4  …  2211  2190  2168  …  
20141028T04:00:00.0  20141027  …  5  …  2  2301  100  543.5  …  545  2189  2154  2167  …  2153 
20141028T04:00:00.0  20141028  …  4  …  1  2225  432  983  …  2567  2592  2598  …  
20141028T04:00:00.0  20141028  …  3  …  1  2155  4355  1235.6  …  2532  2490  2487  … 
What information can I obtain from the root cause analysis?¶
First of all, each forecast is always generated by a different model within a Model Zoo. To look at the forecast and understand how it was constructed, one should always limit the view to only other forecasts generated by the same exact model. Each model is additive in its terms, therefore it is easy to see the impact of each term on the forecast individually. There are 2 possible views  nominal and relative. The first one gives precise information about how much each term contributed to the forecasted value. The relative view decomposes the forecast in a slightly different manner that can give you a better geometrical understanding of how the model is build gradually.

Nominal term_i Value of ith term of the model with model_index used to obtain the forecast . The term can be found in the Model Zoo by model_index and the order number i. It is importnat to mention that the term_1 of the model with the model_index 1 is different than the term_1 of the model with the model_index 2  they are two separate models and have different terms.

Relative yhat_i Essentially gives you a forecast which you would obtain if the model only consisted of the first i terms (different from the sum of the first i terms). The advantage is that the forecasting error decreases with the increasing i and you can see the gradual buildup of the model. Nominal view of terms does not satisfy this property. We can see how important individual terms are for the final forecast and how they influence it. If something goes wrong, we can also easily identify which term is responsible.