25 Years of IIF Time Series Forecasting: A Selective Review

We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985; International Journal of Forecasting 1985-2005). During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.Accuracy measures; ARCH model; ARIMA model; Combining; Count data; Densities; Exponential smoothing; Kalman Filter; Long memory; Multivariate; Neural nets; Nonlinearity; Prediction intervals; Regime switching models; Robustness; Seasonality; State space; Structural models; Transfer function; Univariate; VAR.

Introduction to nonlinearities, business cycles, and forecasting

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Introduction to nonlinearities, business cycles, and forecasting.

Forecasting; Business cycles;

Switch-maintenance gemcitabine after first-line chemotherapy in patients with malignant mesothelioma (NVALT19):an investigator-initiated, randomised, open-label, phase 2 trial

Background Almost all patients with malignant mesothelioma eventually have disease progression after first-line therapy. Previous studies have investigated maintenance therapy, but none has shown a great effect. We aimed to assess the efficacy and safety of switch-maintenance gemcitabine in patients with malignant mesothelioma without disease progression after first-line chemotherapy. Methods We did a randomised, open-label, phase 2 trial in 18 hospitals in the Netherlands (NVALT19). We recruited patients aged older than 18 years with unresectable malignant mesothelioma with no evidence of disease progression after at least four cycles of first-line chemotherapy (with platinum and pemetrexed), who had a WHO performance status of 0-2, adequate organ function, and measurable or evaluable disease. Exclusion criteria were active uncontrolled infection or severe cardiac dysfunction, serious disabling conditions, symptomatic CNS metastases, radiotherapy within 2 weeks before enrolment, and concomitant use of any other drugs under investigation. Patients were randomly assigned (1:1), using the minimisation method, to maintenance intravenous gemcitabine (1250 mg/m(2) on days 1 and 8, in cycles of 21 days) plus supportive care, or to best supportive care alone, until disease progression, unacceptable toxicity, serious intercurrent illness, patient request for discontinuation, or need for any other anticancer agent, except for palliative radiotherapy. A CT scan of the thorax or abdomen (or both) and pulmonary function tests were done at baseline and repeated every 6 weeks. The primary outcome was progression-free survival in the intention-to-treat population. Safety was analysed in all participants who received one or more doses of the study drug or had at least one visit for supportive care. Recruitment is now closed; treatment and follow-up are ongoing. This study is registered with the Netherlands Trial Registry, NTR4132/NL3847. Findings Between March 20, 2014, and Feb 27, 2019, 130 patients were enrolled and randomly assigned to gemcitabine plus supportive care (65 patients [50%]) or supportive care alone (65 patients [50%]). No patients were lost to follow-up; median follow-up was 36.5 months (95% CI 34.2 to not reached), and one patient in the supportive care group withdrew consent. Progression-free survival was significantly longer in the gemcitabine group (median 6.2 months [95% CI 4.6-8.7]) than in the supportive care group (3.2 months [2.8-4.1]; hazard ratio [HR] 0.48 [95% CI 0.33-0.71]; p=0.0002). The benefit was confirmed by masked independent central review (HR 0.49 [0.33-0.72]; p=0.0002). Grade 3-4 adverse events occurred in 33 ( 52%) of 64 patients in the gemcitabine group and in ten (16%) of 62 patients in the supportive care group. The most frequent adverse events were anaemia, neutropenia, fatigue or asthenia, pain, and infection in the gemcitabine group, and pain, infection, and cough or dyspnoea in the supportive care group. One patient (2%) in the gemcitabine group died, due to a treatment-related infection. Interpretation Switch-maintenance gemcitabine, after first-line chemotherapy, significantly prolonged progression-free survival compared with best supportive care alone, among patients with malignant mesothelioma. This study confirms the activity of gemcitabine in treating malignant mesothelioma

Pesticide Exposure of Residents Living Close to Agricultural Fields in the Netherlands:Protocol for an Observational Study

Background: Application of pesticides in the vicinity of homes has caused concern regarding possible health effects in residents living nearby. However, the high spatiotemporal variation of pesticide levels and lack of knowledge regarding the contribution of exposure routes greatly complicates exposure assessment approaches. Objective: The objective of this paper was to describe the study protocol of a large exposure survey in the Netherlands assessing pesticide exposure of residents living close ( Methods: We performed an observational study involving residents living in the vicinity of agricultural fields and residents living more than 500 m away from any agricultural fields (control subjects). Residential exposures were measured both during a pesticide use period after a specific application and during the nonuse period for 7 and 2 days, respectively. We collected environmental samples (outdoor and indoor air, dust, and garden and field soils) and personal samples (urine and hand wipes). We also collected data on spraying applications as well as on home characteristics, participants' demographics, and food habits via questionnaires and diaries. Environmental samples were analyzed for 46 prioritized pesticides. Urine samples were analyzed for biomarkers of a subset of 5 pesticides. Alongside the field study, and by taking spray events and environmental data into account, we developed a modeling framework to estimate environmental exposure of residents to pesticides. Results: Our study was conducted between 2016 and 2019. We assessed 96 homes and 192 participants, including 7 growers and 28 control subjects. We followed 14 pesticide applications, applying 20 active ingredients. We collected 4416 samples: 1018 air, 445 dust (224 vacuumed floor, 221 doormat), 265 soil (238 garden, 27 fields), 2485 urine, 112 hand wipes, and 91 tank mixtures. Conclusions: To our knowledge, this is the first study on residents' exposure to pesticides addressing all major nondietary exposure sources and routes (air, soil, dust). Our protocol provides insights on used sampling techniques, the wealth of data collected, developed methods, modeling framework, and lessons learned. Resources and data are open for future collaborations on this important topic

Asymptotically Informative Prior for Bayesian Analysis

In classical Bayesian inference the prior is treated as fixed, it is asymptotically negligible, thus any information contained in the prior is ignored from the asymptotic first order result. However, in practice often an informative prior is summarized from previous similar or the same kind of studies, which contains non-negligible information for the current study. Here, different from traditional Bayesian point of view, we treat such prior to be non-fixed. In particular, we give the data sizes used in previous studies for the prior the same status as the size of the current dataset, viewing both sample sizes as increasing to infinity in the asymptotic study. Thus the prior is asymptotically non-negligible, and its original effects are ressumed under this view. Consequently, Bayesian inference using such prior is more efficient, as it should be, than that regarded under the existing setting. We study some basic properties of Bayesian estimators using such priors under convex losses and the 0-1 loss, and illustrate the method by an example via simulation

On the automatic identification of unobserved components models

Automatic identi cation of time series models is a necessity once the big data era has come and is staying among us. This has become obvious for many companies and public entities that has passed from a crafted analysis of each individual problem to handle a tsunami of information that has to be processed e ciently, online and in record time. Automatic identi cation tools has never been tried out on Unobserved Components models (UC). This chapter shows how information criteria, such as Akaike's or Schwarz's, are rather useful for model selection within the UC family. The di culty lies, however, on choosing an appropriate and as general as possible set of models to search in. A set too narrow would render poor forecast accuracy, while a set too wide would be highly time consuming. The forecasting results suggest that UC models are powerful potential forecasting competitors to other well-known methods. Though there are several pieces of software available for UC modeling, this is the rst implementation of an automatic algorithm for this class of models, to the best of the authors knowledge