GB2616253A - Systems, methods and apparatus for hierarchical forecasting - Google Patents

Systems, methods and apparatus for hierarchical forecasting Download PDF

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Publication number
GB2616253A
GB2616253A GB2201676.0A GB202201676A GB2616253A GB 2616253 A GB2616253 A GB 2616253A GB 202201676 A GB202201676 A GB 202201676A GB 2616253 A GB2616253 A GB 2616253A
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United Kingdom
Prior art keywords
hierarchy
forecast
weight matrix
computer
node
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GB2201676.0A
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Haji Soleimani Behrouz
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Kinaxis Inc
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Kinaxis Inc
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management

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  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Development Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Finance (AREA)
  • Accounting & Taxation (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Data Mining & Analysis (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

Systems and methods for reconciling a forecast within a hierarchy, comprising a pre¬ processing module and a forecast reconciliation module. The pre-processing module reconstructs the structure of the hierarchy and captures the relationship between the nodes of the hierarchy in a summation matrix S. The forecast reconciliation matrix uses S, a weight matrix W (that reflects a weighting scheme between the nodes) and a base forecast to optimize the overall forecast error using a least squares procedure. The reconciled forecast has a zero consistency error.

Claims (20)

1. A computer-implemented method for forecast reconciliation in a hierarchy, the method comprising the steps of: receiving, by a pre-processing module, data related to the hierarchy; generating, by the pre-processing module, the hierarchy based on the data and a summation matrix related to a structure of the hierarchy; receiving, by a forecast reconciliation module, a base forecast of the hierarchy, the summation matrix and a weight matrix, the weight matrix reflecting a weighting scheme for each node of the hierarchy, the weight matrix generated by either the pre-processing module or the forecast reconciliation module; generating, by the forecast reconciliation module, a reconciled forecast based on a least squares optimization technique for projecting the base forecast onto a bottom level of the hierarchy, subject to a constraint on each node of the bottom level of the hierarchy.
2. The computer-implemented method of claim 1, wherein the reconciled forecast is based on a non-negative least squares optimization technique.
3. The computer-implemented method of claim 1, wherein the reconciled forecast is based on an iterative optimization in which each node of the bottom level forecast is bound within a respective range.
4. The computer-implemented method of claim 1, wherein each entry of the weight matrix is related to one or more metrics of the hierarchy.
5. The computer-implemented method of claim 4, wherein the weight matrix is diagonal.
6. The computer-implemented method of claim 1, wherein each entry of the weight matrix is related to a forecast error of each node of the hierarchy.
7. The computer-implemented method of claim 1, wherein the pre-processing module performs at least one of: i) removing one or more nodes of the hierarchy that have a zero value or a value less than a threshold value; ii) filling one or more missing records of the hierarchy based on sibling information; and iii) extracting rolling features at all levels of the hierarchy.
8. A computing apparatus, the computing apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the apparatus to the steps of: receive, by a pre-processing module, data related to a hierarchy; generate, by the pre-processing module, the hierarchy based on the data and a summation matrix related to a structure of the hierarchy; receive, by a forecast reconciliation module, a base forecast of the hierarchy, the summation matrix and a weight matrix, the weight matrix reflecting a weighting scheme for each node of the hierarchy, the weight matrix generated by either the pre processing module or the forecast reconciliation module; generate, by the forecast reconciliation module, a reconciled forecast based on a least squares optimization technique for projecting the base forecast onto a bottom level of the hierarchy, subject to a constraint on each node of the bottom level of the hierarchy.
9. The computing apparatus of claim 8, wherein the reconciled forecast is based on a non negative least squares optimization technique.
10. The computing apparatus of claim 8, wherein the reconciled forecast is based on an iterative optimization in which each node of the bottom level forecast is bound within a respective range.
11. The computing apparatus of claim 8, wherein each entry of the weight matrix is related to one or more metrics of the hierarchy.
12. The computing apparatus of claim 11, wherein the weight matrix is diagonal.
13. The computing apparatus of claim 8, wherein each entry of the weight matrix is related to a forecast error of each node of the hierarchy.
14. The computing apparatus of claim 8, wherein the pre-processing module performs at least one of: i) remove one or more nodes of the hierarchy that have a zero value or a value less than a threshold value; ii) fill one or more missing records of the hierarchy based on sibling information; and iii) extract rolling features at all levels of the hierarchy.
15. A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to: receive, by a pre-processing module, data related to a hierarchy; generate, by the pre-processing module, the hierarchy based on the data and a summation matrix related to a structure of the hierarchy; receive, by a forecast reconciliation module, a base forecast of the hierarchy, the summation matrix and a weight matrix, the weight matrix reflecting a weighting scheme for each node of the hierarchy, the weight matrix generated by either the pre-processing module or the forecast reconciliation module; generate, by the forecast reconciliation module, a reconciled forecast based on a least squares optimization technique for projecting the base forecast onto a bottom level of the hierarchy, subject to a constraint on each node of the bottom level of the hierarchy.
16. The computer-readable storage medium of claim 15, wherein the reconciled forecast is based on a non-negative least squares optimization technique.
17. The computer-readable storage medium of claim 15, wherein the reconciled forecast is based on an iterative optimization in which each node of the bottom level forecast is bound within a respective range.
18. The computer-readable storage medium of claim 15, wherein each entry of the weight matrix is related to one or more metrics of the hierarchy.
19. The computer-readable storage medium of claim 18, wherein the weight matrix is diagonal.
20. The computer-readable storage medium of claim 15, wherein each entry of the weight matrix is related to a forecast error of each node of the hierarchy.
GB2201676.0A 2021-07-27 2021-07-27 Systems, methods and apparatus for hierarchical forecasting Pending GB2616253A (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CA2021/051049 WO2023004488A1 (en) 2021-07-27 2021-07-27 Systems, methods and apparatus for hierarchical forecasting

Publications (1)

Publication Number Publication Date
GB2616253A true GB2616253A (en) 2023-09-06

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Application Number Title Priority Date Filing Date
GB2201676.0A Pending GB2616253A (en) 2021-07-27 2021-07-27 Systems, methods and apparatus for hierarchical forecasting

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JP (1) JP2024529800A (en)
CA (1) CA3148578A1 (en)
DE (1) DE112021000081T5 (en)
GB (1) GB2616253A (en)
WO (1) WO2023004488A1 (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100254707A1 (en) * 2009-04-07 2010-10-07 Peng Jan-Wen Dynamic Bandwidth Allocation Method of Ethernet Passive Optical Network
US20120197900A1 (en) * 2010-12-13 2012-08-02 Unisys Corporation Systems and methods for search time tree indexes
US20140257778A1 (en) * 2013-03-06 2014-09-11 Sas Institute Inc. Devices for Forecasting Ratios in Hierarchies
US20160260052A1 (en) * 2015-03-06 2016-09-08 Wal-Mart Stores, Inc. System and method for forecasting high-sellers using multivariate bayesian time series
US20180225600A1 (en) * 2017-02-03 2018-08-09 The Dun And Bradstreet Corporation System and method for assessing and optimizing master data maturity
CN109034905A (en) * 2018-08-03 2018-12-18 四川长虹电器股份有限公司 The method for promoting sales volume prediction result robustness

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8352215B2 (en) * 2009-10-23 2013-01-08 Sas Institute Inc. Computer-implemented distributed iteratively reweighted least squares system and method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100254707A1 (en) * 2009-04-07 2010-10-07 Peng Jan-Wen Dynamic Bandwidth Allocation Method of Ethernet Passive Optical Network
US20120197900A1 (en) * 2010-12-13 2012-08-02 Unisys Corporation Systems and methods for search time tree indexes
US20140257778A1 (en) * 2013-03-06 2014-09-11 Sas Institute Inc. Devices for Forecasting Ratios in Hierarchies
US20160260052A1 (en) * 2015-03-06 2016-09-08 Wal-Mart Stores, Inc. System and method for forecasting high-sellers using multivariate bayesian time series
US20180225600A1 (en) * 2017-02-03 2018-08-09 The Dun And Bradstreet Corporation System and method for assessing and optimizing master data maturity
CN109034905A (en) * 2018-08-03 2018-12-18 四川长虹电器股份有限公司 The method for promoting sales volume prediction result robustness

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Publication number Publication date
DE112021000081T5 (en) 2023-03-23
JP2024529800A (en) 2024-08-14
CA3148578A1 (en) 2023-01-27
WO2023004488A1 (en) 2023-02-02

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