Sparse Time Series Demand Forecasting for Intermittent Availability
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Publicerad
2023
Författare
Helgesson, Oscar
Laszlo, Norbert
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis addresses the challenge of forecasting sales for individual perishable
markdown products using historical sales data and other relevant features in the
form of time series. The target time series, i.e., daily sales of marked-down units
of a certain product, is intermittent, sparse, and highly irregular, and sales can
only occur if the marked-down product is available. To solve the problem, various
methods were evaluated, ranging from well-established statistical models to newer
deep learning-based models. This thesis proposes an interpretable novel method
that improves the Temporal Fusion Transformer model with cluster encodings by
applying random convolutional kernel transformations to time series.
The study found that the compared deep learning models outperformed the baseline
statistical models, particularly the RNN and Temporal Fusion Transformer.
The novel approach of clustering the markdown series based on markdown features
showed no significant change in performance regarding day-to-day prediction.
However, it did show a significant improvement in multi-horizon aggregated predictions.
Moreover, using clustering resulted in decreased time in training the models.
Overall, the results suggest that deep learning models and the Temporal Fusion
Transformer with added cluster encodings are promising models for predicting intermittent
series with known available inventory. This study has practical implications
for retailers and businesses that sell perishable products. Accurately forecasting sales
of markdown products can help reduce waste and optimize inventory management,
resulting in cost savings and increased profitability.
Beskrivning
Ämne/nyckelord
Time Series Forecasting, Intermittent Time Series, Intermittent Forecasting, Machine Learning, Deep Learning, Temporal Fusion Transformer, Time Series Clustering, ROCKET