Improving forecasting by estimating time series structural components across multiple frequencies

Transcription

1 Improving forecating y etimating time erie tructura component acro mutipe frequencie Nikoao Kourentze, Fotio Petropouo and Juan R Trapero Internationa Journa of Forecating, 30 (2014), p Thi work wa upported y the Lancater Univerity Management Schoo reearch grant cheme

2 Buine Forecating Forecating i crucia for evera operation of an organiation Short- and ong-term ojective Demand and inventory panning Capacity panning Pricing and marketing trategy panning Budgeting etc Requirement for arge numer of forecat Automa7on Iue for organiation: Forecat accuracy Forecat reiaiity/routne Forecat reconciiation

3 An organiation ha to often produce forecat for: Short term operationa horizon Medium term tactica horizon Long term trategic horizon Buine Forecating The proem of tempora reconciiation We know that different forecating mode are etter for different forecat horizon We ao know that it hep to forecat ong horizon uing aggregate data (M,A d,a) - AIC: Good for hort term eaonaity with minima trend Month 8 x (A,A,N) - AIC: d eaonaity! 8 10 ear Thee forecat often do not agree, even though they are aed on the ame data! How to aign panning, deciion making and operation? Same data, ut different time granuarity; good for ong term trong trend and no

4 Tempora Aggregation Benefit With tempora aggregation we can change the propertie of a time erie Different component ecome weaker or tronger, eg eaonaity Monthy Quartery Haf-annuay 9-monthy Annuay Aggregation Leve 1 Aggregation Leve 3 Aggregation Leve 6 Aggregation Leve 9 Aggregation Leve Strong eaonaity Seaonaity Weak eaonaity No eaonaity No eaonaity Leve of aggregation Fitting a mode at each tempora aggregation eve wi capture different type of information If thee are comined then there can e major accuracy enefit Such comined forecat are reconcied acro a time cae (hort, medium, ong)

5 Tempora Aggregation & Reconciiation MAPA Mutipe Aggregation Prediction Agorithm Step 1: Aggregation Step 2: Forecating Step 3: Comination + ˆ k = 2 k = 3 k = K 1 K 1 K 1 K & Strengthen and attenuate time erie component Etimate different mode, taking advantage of the variou view of the erie Comine information make mode eection and parameteriation rout

6 Reut & Concuion 1 Propoed agorithm can provide temporay reconcied forecat Short, medium and ong term panning are aed on the ame forecat, rather than uing different mode and forecat Simpifie deciion making 2 Accuracy uperior to uing inge mode, or mutipe mode (one for each time granuarity) Initia prototype +5% accuracy over et performance in the iterature Current mode more than 10% accuracy improvement, acro a forecat horizon Particuary accurate on ong term prediction 3 Reconciiation method i mode independent Ue current forecating method/ytem 4 Reduce rik of eecting wrong forecating mode or parameter Detaied anayi, finding and reference in the paper: