Small-Scale Demand Forecasting: Exploring the Potential of Machine Learning and Hierarchical Reconciliation
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2023
Författare
Zandhoff Westerlund, Viking
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Demand 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.
Beskrivning
Ämne/nyckelord
Time series forecasting, Demand forecasting, Hierarchical time series, Temporal Fusion Transformer, LightGBM, Minimum trace reconciliation