Root cause analysis
Root cause analysis¶
Root cause analysis (RCA) can be valuable information provided over a forecasting result. A user can trigger the calculation of RCA results for an already finished forecasting job by calling the RCA endpoint. This allows users to see why the forecast looks the way it does and how the Model Zoo is constructed.
The table below gives an overview of the output to expect of the tabular response for forecasting jobs of RCA type:
Structure of the output¶
timestamp  term_1  term_2  term_3  …  term_N  yhat_1  yhat_2  yhat_3  …  yhat_N 

20141028T03:00:00.0  2546  900  943.05  …  624  1943  1984  1987  …  1996 
20141028T04:00:00.0  2451  5000  5409.6  …  2195  2104  2089  …  
20141028T04:00:00.0  2103  200  65363.4  …  2211  2190  2168  …  
20141028T04:00:00.0  2301  100  543.5  …  545  2189  2154  2167  …  2153 
20141028T04:00:00.0  2225  432  983  …  2567  2592  2598  …  
20141028T04:00:00.0  2155  4355  1235.6  …  2532  2490  2487  … 
What information can I obtain from the root cause analysis?¶
First of all, each forecast is can be 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  that is why the model's index is a required parameter in the rca endpoint. Each model is additive in its terms  therefore, it is easy to see the impact of each term on the forecast individually. There are two possible views: a nominal view and a relative view. The nominal view gives precise information about how much each term contributed to the forecasted value. The relative view decomposes the forecast slightly differently, giving a better geometrical understanding of how the model is gradually built.

Nominal term_i: the value of the ith term of the model of a chosen 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 essential 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: this essentially equals the forecast which would be obtained if the model only consisted of the first i terms (different from the sum of the first i terms). The forecasting error thus decreases with increasing i, showing the gradual buildup of the model. The nominal view of terms does not satisfy this property. This property visualizes how important individual terms are for the final forecast and how they influence it. If something goes wrong, this allows users to easily identify which term is responsible.