Time Series Forecasting Using Neural Networks Minimizing Food Waste By Forecasting Demand in Retail Sales
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2021
Författare
Mahmoudyan, Morad
Zeqiri, Arianit
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
A third of the food produced for human consumption is wasted annually, amounting
to 1.3 billion tons of food waste per year [10]. Minimizing these enormous quantities
of waste would not only be beneficial for the planet but also help feed an ever
increasing human population. One way of minimizing this waste is by helping retail
sellers better plan their logistical operations by accurately predicting the demand
of goods using forecasting models. The procedure in time series forecasting has
traditionally been to use statistical models such as autoregressive integrated moving
average (ARIMA) and exponential smoothing methods. These methods have been
shown to be limited in their predictive capabilities as the sizes of data sets and the
number of variables increases. In the last decade new machine learning algorithms
have been used extensively in various fields and has opened up the door for utilization
of models based on neural networks in time series forecasting. A subset of these
new machine learning algorithms, such as transformer based models and recurrent
neural networks, have been proven to be especially suitable for temporal data. In
this thesis we investigate two models, Temporal Fusion Transformers (TFTs) and
Deep Temporal Convolutional Networks (DeepTCNs), showcasing their abilities to
generate accurate forecasts of retail sales on a real-world data set. We demonstrate,
using several metrics, their ability to outperform baseline models.
Beskrivning
Ämne/nyckelord
forecasting , machine learning , neural networks , food waste , retail sales , dilated convolution , temporal fusion transformer , temporal convolutional network