Small-Scale Demand Forecasting: Exploring the Potential of Machine Learning and Hierarchical Reconciliation

Visa enkel post
dc.contributor.authorZandhoff Westerlund, Viking
dc.contributor.departmentChalmers tekniska högskola / Institutionen för fysiksv
dc.contributor.departmentChalmers University of Technology / Department of Physicsen
dc.contributor.examinerGranath, Mats
dc.date.accessioned2023-06-27T12:20:59Z
dc.date.available2023-06-27T12:20:59Z
dc.date.issued2023
dc.date.submitted2023
dc.description.abstractDemand forecasting plays an important role in facilitating data-driven decision making for businesses, particularly in domains such as inventory planning and re source allocation. While traditional forecasting models such as exponential smooth ing and autoregressive models have long been prevalent in the time series forecasting domain, recent research has been increasingly focused on more complex machine learning-based models. These complex models offer great potential and flexibility, but they require large amounts of data to achieve optimal performance. In this thesis, I explored viable approaches for constructing accurate forecasting models for a young company in the industrial production industry who wants to predict their future demand, while facing the challenge of limited data availability. The analy sis in this thesis involved comparing the predictive performance of state-of-the-art machine learning models, such as the Temporal Fusion Transformer (TFT) and the LightGBM to an exponential smoothing state-space model. Furthermore, I investi gated whether the hierarchical structure of the time series data could be exploited through forecast reconciliation to further increase forecasting accuracy. My findings indicate that both the TFT and LightGBM demonstrate superior forecasting accu racy, improving the average forecast accuracy with 43.1 % and 33.2 % respectively, compared to the exponential smoothing model. However, the TFT displayed incon sistent performance results, suggesting its unreliability. Moreover, the results show that while hierarchical forecast reconciliation does not enhance forecast accuracy, it corrects incoherency between forecasts without compromising accuracy, which is of great value.
dc.identifier.coursecodeTIFX05
dc.identifier.urihttp://hdl.handle.net/20.500.12380/306447
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectTime series forecasting, Demand forecasting, Hierarchical time series, Temporal Fusion Transformer, LightGBM, Minimum trace reconciliation
dc.titleSmall-Scale Demand Forecasting: Exploring the Potential of Machine Learning and Hierarchical Reconciliation
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
Ladda ner
Original bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
Masters_Thesis_Viking_Zandhoff_Westerlund_Final.pdf
Storlek:
1.78 MB
Format:
Adobe Portable Document Format
Beskrivning:
Ladda ner
License bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
license.txt
Storlek:
2.35 KB
Format:
Item-specific license agreed upon to submission
Beskrivning:
Ladda ner
Samling
Examensarbeten för masterexamen