Time Series Forecasting Using Neural Networks Minimizing Food Waste By Forecasting Demand in Retail Sales

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dc.contributor.authorMahmoudyan, Morad
dc.contributor.authorZeqiri, Arianit
dc.contributor.departmentChalmers tekniska högskola / Institutionen för fysiksv
dc.contributor.examinerGranath, Mats
dc.contributor.supervisorGraf, Anton
dc.contributor.supervisorTorabi, Sina
dc.date.accessioned2021-06-17T07:04:57Z
dc.date.available2021-06-17T07:04:57Z
dc.date.issued2021sv
dc.date.submitted2020
dc.description.abstractA 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.sv
dc.identifier.coursecodeTIFX05sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/302581
dc.language.isoengsv
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectforecastingsv
dc.subjectmachine learningsv
dc.subjectneural networkssv
dc.subjectfood wastesv
dc.subjectretail salessv
dc.subjectdilated convolutionsv
dc.subjecttemporal fusion transformersv
dc.subjecttemporal convolutional networksv
dc.titleTime Series Forecasting Using Neural Networks Minimizing Food Waste By Forecasting Demand in Retail Salessv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
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