Forecast-Coherent QoS/SLA Budgeting for Fiscal Data Pipelines: An Analytical Study with Holt–Winters and ARIMA(0,2,2)

Misspecifications of the model are infrequent cause for incorrectness in the prediction. In production revenue streams: the age and quality of arriving information is often the binding constraint (delayed postings, batchiness ± timing around month/quarter ends, tiny but systematic losses within the transport/ETL pipeline).[10] In this paper, the authors recommend regeneration in the hope of QoS/SLA descriptor values that take into account forecast error directly rather than an heuristic (i.e., delay/jitter/loss) level used as a substitute for such objective parameter. Applying a dense (i.e., compact quarterly) anchor for 2024–2025 and the classical models (multiplicative Holt–Winters, ARIMA(0,2,2)), we obtain a simple sensitivity map between publication delay L (days in quarter units) and the increase in the quarterly MAPE: ΔMAPE(L)=c_m•L/90 (pp). The author calculates that 30 delays inflate the quarterly MAPE by ≈1.1 pp for Holt–Winters, 1.0 pp for ARIMA; deep models would perform at 1.5 and 1.8 pp, respectively— their accuracy (It looks like) is bought with precision in a timely manner! These sensitivities allow for final SLA budgets – e.g., p95 latency ≤ 10 ms and loss ≤ 0.01 % for quarter-critical streams – that maintain ΔMAPE to no more than a 0.5-pp (percentage point) tolerance when enforced most tightly near reporting closes. The contribution is practical: a recipe to transform network knobs into guarantees for forecasting, and one can take its steps for execution when there are no monthly microdata.

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