Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/171611
Title: SPATIO-TEMPORAL MODELS FOR FORECASTING FOOD DELIVERY DEMAND
Authors: ANG PENG SENG
Keywords: Spatio-temporal demand prediction
Time Series, Clustering
Vector Autoregressive models
Spatio-Temporal Autoregressive models
Issue Date: 6-Apr-2020
Citation: ANG PENG SENG (2020-04-06). SPATIO-TEMPORAL MODELS FOR FORECASTING FOOD DELIVERY DEMAND. ScholarBank@NUS Repository.
Abstract: Many real industry problems involves spatio-temporal demand prediction, which requires predicting demand at a certain time across different locations. Most of the demand data are not continuous variables but are instead count variables, like the number of orders and number of transactions. Previous research studies have mainly implemented neural network methods for spatio-temporal problems. In this paper however, we would explore more interpretable methods and hence will focus on how we can apply classical time series models, such as Vector Autoregressive (VAR), Spatio-Temporal Autoregressive (STAR) models to exploit spatial correlations as well as also introduce a 3-step approach to improve forecast accuracy.
URI: https://scholarbank.nus.edu.sg/handle/10635/171611
Appears in Collections:Bachelor's Theses

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