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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
2014-10-28T03:00:00.0 2546 900 943.05 624 1943 1984 1987 1996
2014-10-28T04:00:00.0 2451 5000 5409.6 2195 2104 2089
2014-10-28T04:00:00.0 2103 200 65363.4 2211 2190 2168
2014-10-28T04:00:00.0 2301 100 543.5 545 2189 2154 2167 2153
2014-10-28T04:00:00.0 2225 432 983 2567 2592 2598
2014-10-28T04:00:00.0 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.

  1. Nominal term_i: the value of the i-th 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.

  2. 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 build-up 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.