Forecasting hierarchical or grouped time series usually involves two steps: computing base forecasts and reconciling the forecasts. Base forecasts can be computed by popular time series forecasting methods such as Exponential Smoothing (ETS) and Autoregressive Integrated Moving Average (ARIMA) models. The reconciliation step is a linear process that adjusts the base forecasts to ensure they are coherent. However using ETS or ARIMA for base forecasts can be computationally challenging when there are a large number of series to forecast, as each model must be numerically optimized for each series. We propose a linear model that avoids this computational problem and handles the forecasting and reconciliation in a single step. The proposed method is very flexible in incorporating external data, handling missing values and model selection. We illustrate our approach using two datasets: monthly Australian domestic tourism and daily Wikipedia pageviews. We compare our approach to reconciliation using ETS and ARIMA, and show that our approach is much faster while providing similar levels of forecast accuracy.
Analytics is important for education planning. Deploying forecasting analytics requires management information systems (MISs) that collect the needed data and deliver the forecasts to stakeholders. A critical question is whether the data collected by a system is adequate for producing the analytics for decision making. We describe the case of a new education MIS in Taiwan, where the population of preschool children in different school districts is constantly changing. These changes challenge school resource planning, especially in terms of teacher hiring. The bureaus of education in charge of resource allocation are in need of accurate school-level one-to-five-year-ahead forecasts of the number of incoming first-grade classrooms. The Ministry of Education therefore launched a K–9 student data management system (k9sdms) that allows schools to directly update data on existing and prospective students. We evaluate whether using this system supports the goal of generating one-to-five-year-ahead forecasts, thereby assessing the value of the MIS for its intended usage. Using data until 2014, we developed a forecasting model for the number of first-grade classrooms at each school in Taiwan in 2015–2019. The quality of forecasts shows that k9sdms can produce valuable results, thereby achieving its purpose.
The COVID-19 data analysis is essential for policymakers to analyze the outbreak and manage the containment. Many approaches based on traditional time series clustering and forecasting methods, such as hierarchical clustering and exponential smoothing, have been proposed to cluster and forecast the COVID-19 data. However, most of these methods do not scale up with the high volume of cases. Moreover, the interactive nature of the application demands further critically complex yet compelling clustering and forecasting techniques. In this paper, we propose a web-based interactive tool to cluster and forecast the available data of Taiwan COVID-19 confirmed infection cases. We apply the Model-based (MOB) tree and domain-relevant attributes to cluster the dataset and display forecasting results using the Ordinary Least Square (OLS) method. In this OLS model, we apply a model produced by the MOB tree to forecast all series in each cluster. Our user-friendly parametric forecasting method is computationally cheap. A web app based on R’s Shiny App makes it easier for practitioners to find clustering and forecasting results while choosing different parameters such as domain-relevant attributes. These results could help in determining the spread pattern and be utilized by medical researchers.
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