Forecast horizon aggregation in integer autoregressive moving average (INARMA) models

This paper addresses aggregation in integer autoregressive moving average (INARMA) models. Although aggregation in continuous-valued time series has been widely discussed, the same is not true for integer-valued time series. Forecast horizon aggregation is addressed in this paper. It is shown that the overlapping forecast horizon aggregation of an INARMA process results in an INARMA process. The conditional expected value of the aggregated process is also derived for use in forecasting. A simulation experiment is conducted to assess the accuracy of the forecasts produced by the aggregation method and to compare it to the accuracy of cumulative h-step ahead forecasts over the forecasting horizon. The results of an empirical analysis are also provided

Reproducibility in forecasting research

The importance of replication has been recognised across many scientific disciplines. Reproducibility is a necessary condition for replicability, because an inability to reproduce results implies that the methods have not been specified sufficiently, thus precluding replication. This paper describes how two independent teams of researchers attempted to reproduce the empirical findings of an important paper, ‘‘Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy’’ (Miller & Williams, 2003). The two teams proceeded systematically, reporting results both before and after receiving clarifications from the authors of the original study. The teams were able to approximately reproduce each other’s results, but not those of Miller and Williams. These discrepancies led to differences in the conclusions as to the conditions under which seasonal damping outperforms classical decomposition. The paper specifies the forecasting methods employed using a flowchart. It is argued that this approach to method documentation is complementary to the provision of computer code, as it is accessible to a broader audience of forecasting practitioners and researchers. The significance of this research lies not only in its lessons for seasonal forecasting but also, more generally, in its approach to the reproduction of forecasting research

OR in Spare Parts Management:A Review

Abstract Spare parts are held to reduce the consequences of equipment downtime, playing an important role in achieving the desired equipment availability at a minimum economic cost. In this paper, a framework for OR in spare parts management is presented, based on the product lifecycle process and including the objectives, main tasks, and OR disciplines for supporting spare parts management. Based on the framework, a systematic literature review of OR in spare parts management is undertaken, and then a comprehensive investigation of each OR discipline's contribution is given. The gap between theory and practice of spare parts management is investigated from the perspective of software integration, maintenance management information systems and adoption of new OR methods in software. Finally, as the result of this review, an extended version of the framework is proposed and a set of future research directions is discussed

The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes

Various approaches have been considered in the literature to improve demand forecasting in supply chains. Among these approaches, non-overlapping temporal aggregation has been shown to be an effective approach that can improve forecast accuracy. However, the benefit of this approach has been shown only under single exponential smoothing (when it is a non-optimal method) and no theoretical analysis has been conducted to look at the impact of this approach under optimal forecasting. This paper aims to bridge this gap by analysing the impact of temporal aggregation on supply chain demand and orders when optimal forecasting is used. To do so, we consider a two-stage supply chain (e.g. a retailer and a manufacturer) where the retailer faces an autoregressive moving average demand process of order (1,1) -ARMA(1,1)- that is forecasted by using the optimal Minimum Mean Squared Error (MMSE) forecasting method. We derive the analytical expressions of the mean squared forecast error (MSE) at the retailer and the manufacturer levels as well as the bullwhip ratio when the aggregation approach is used. We numerically show that, although the aggregation approach leads to an accuracy loss at the retailer's level, it may result in a reduction of the MSE at the manufacturer level up to 90% and a reduction of the bullwhip effect in the supply chain that can reach up to 84% for high lead-times

Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem

Abstract Short term probabilistic load forecasting is essential for any power generating utility. This paper discusses an application of partially linear additive quantile regression models for predicting short term electricity demand during the peak demand hours (i.e. from 18:00 to 20:00) using South African data for January 2009 to June 2012. Additionally the bounded variable mixed integer linear programming technique is used on the forecasts obtained in order to find an optimal number of units to commit (switch on or off. Variable selection is done using the least absolute shrinkage and selection operator. Results from the unit commitment problem show that it is very costly to use gas fired generating units. These were not selected as part of the optimal solution. It is shown that the optimal solutions based on median forecasts ( Q 0.5 quantile forecasts) are the same as those from the 99th quantile forecasts except for generating unit g 8 c , which is a coal fired unit. This shows that for any increase in demand above the median quantile forecasts it will be economical to increase the generation of electricity from generating unit g 8 c . The main contribution of this study is in the use of nonlinear trend variables and the combining of forecasting with the unit commitment problem. The study should be useful to system operators in power utility companies in the unit commitment scheduling and dispatching of electricity at a minimal cost particularly during the peak period when the grid is constrained due to increased demand for electricity

Cerebellar c9RAN proteins associate with clinical and neuropathological characteristics of C9ORF72 repeat expansion carriers.

Clinical and neuropathological characteristics associated with G4C2 repeat expansions in chromosome 9 open reading frame 72 (C9ORF72), the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia, are highly variable. To gain insight on the molecular basis for the heterogeneity among C9ORF72 mutation carriers, we evaluated associations between features of disease and levels of two abundantly expressed "c9RAN proteins" produced by repeat-associated non-ATG (RAN) translation of the expanded repeat. For these studies, we took a departure from traditional immunohistochemical approaches and instead employed immunoassays to quantitatively measure poly(GP) and poly(GA) levels in cerebellum, frontal cortex, motor cortex, and/or hippocampus from 55 C9ORF72 mutation carriers [12 patients with ALS, 24 with frontotemporal lobar degeneration (FTLD) and 19 with FTLD with motor neuron disease (FTLD-MND)]. We additionally investigated associations between levels of poly(GP) or poly(GA) and cognitive impairment in 15 C9ORF72 ALS patients for whom neuropsychological data were available. Among the neuroanatomical regions investigated, poly(GP) levels were highest in the cerebellum. In this same region, associations between poly(GP) and both neuropathological and clinical features were detected. Specifically, cerebellar poly(GP) levels were significantly lower in patients with ALS compared to patients with FTLD or FTLD-MND. Furthermore, cerebellar poly(GP) associated with cognitive score in our cohort of 15 patients. In the cerebellum, poly(GA) levels similarly trended lower in the ALS subgroup compared to FTLD or FTLD-MND subgroups, but no association between cerebellar poly(GA) and cognitive score was detected. Both cerebellar poly(GP) and poly(GA) associated with C9ORF72 variant 3 mRNA expression, but not variant 1 expression, repeat size, disease onset, or survival after onset. Overall, these data indicate that cerebellar abnormalities, as evidenced by poly(GP) accumulation, associate with neuropathological and clinical phenotypes, in particular cognitive impairment, of C9ORF72 mutation carriers