Cost-Based Model of Seasonal Production, with Application to Milk Policy, A

Milk production is seasonal in many European countries. While quantity seasonality poses capacity management problems for dairy processors, a European Union policy goal is to reduce price seasonality. After developing a model of endogenous seasonality, we study the effects of three E.U. policies on production decisions. These are private storage subsidies, production removals, and production quotas. When cost functions are seasonal in a specified way, then arbitrage opportunities interact with storage subsidies to reduce both price and consumption seasonality. But production seasonality likely increases because storage subsidies promote temporal market integration. Conditions are identified under which product market interventions increase quantity seasonality.efficiency, market intervention, quota, stabilization, storage subsidies.

Descriptive Seasonal Adjustment by Minimizing Perturbations

The seasonal adjustment method proposed by Schlicht (1981) can be viewed as a method that minimizes non-stochastic deviations (perturbations). This interpretation gives rise to a critique of the seasonality criterion used there. A new seasonality criterion is proposed that avoids these shortcomings, and the resulting seasonal adjustment method is givenseasonal adjustment; seasonality; smoothing; spline; descriptive decomposition

Seasonality of Deaths in the U.S. by Age and Cause

In this paper, we analyze seasonality of deaths by age and cause in the U.S. using public use files for the years 1994 to 1998 by the methods of regression and a variation of Census Method II. We answer the following questions: For each age cohort, how much does each cause of death contribute to seasonality of deaths? What is the reason for the variation in seasonality of deaths with respect to age? We also analyze death records of Social Security Administration over a longer time period to examine how seasonality of deaths has changed since the mid-1970’s. We found that in general, the degree of seasonality in deaths has decreased over time for younger cohorts and has increased over time for older cohorts.mortality, seasonality

Descriptive Seasonal Adjustment by Minimizing Perturbations

The seasonal adjustment method proposed by Schlicht (1981) can be viewed as a method that minimizes non-stochastic deviations (perturbations). This interpretation gives rise to a critique of the seasonality criterion used there. A new seasonality criterion is proposed that avoids these shortcomings, and the resulting seasonal adjustment method is give

Dates of birth and seasonal changes in well-being among 4904 subjects completing the seasonal pattern assessment questionnaire

Background: Abnormal distributions of birthdates, suggesting intrauterine aetiological factors, have been found in several psychiatric disorders, including one study of out-patients with Seasonal Affective Disorder (S.A.D.). We investigated birthdate distribution in relation to seasonal changes in well-being among a cohort who had completed the Seasonal Pattern Assessment Questionnaire (SPAQ). Method: A sample of 4904 subjects, aged 16 to 64, completed the SPAQ. 476 were cases of S.A.D. on the SPAQ and 580 were cases of sub-syndromal S.A.D. (S-S.A.D.). 92 were interview confirmed cases of S.A.D. Months and dates of birth were compared between S.A.D. cases and all others, between S.A.D. and S-S.A.D. cases combined and all others, and between interview confirmed cases and all others. Seasonality, as measured through seasonal fluctuations in well-being on the Global Seasonality Scores (GSS) of the SPAQ, was compared for all subjects by month and season of birth. Results: There was no evidence of an atypical pattern of birthdates for subjects fulfilling criteria for S.A.D., for the combined S.A.D. / S-S.A.D. group or for interview confirmed cases. There was also no relationship between seasonality on the GSS and month or season of birth. Limitations: Diagnoses of S.A.D. made by SPAQ criteria are likely to be overinclusive. Conclusion: Our findings differ from studies of patients with more severe mood disorders, including psychiatric out-patients with S.A.D. The lack of association between seasonality and birthdates in our study adds credence to the view that the aetiology of S.A.D. relates to separable factors predisposing to affective disorders and to seasonality

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.Comment: Accepted to the thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019), 9 pages, 5 figure

Geographic body size variation in ectotherms: effects of seasonality on an anuran from the southern temperate forest

Indexación: Web of Science; Scopus.Background: Body size variation has played a central role in biogeographical research, however, most studies have aimed to describe trends rather than search for underlying mechanisms. In order to provide a more comprehensive understanding of the causes of intra-specific body size variation in ectotherms, we evaluated eight hypotheses proposed in the literature to account for geographical body size variation using the Darwin's frog (Rhinoderma darwinii), an anuran species widely distributed in the temperate forests of South America. Each of the evaluated hypotheses predicted a specific relationship between body size and environmental variables. The level of support for each of these hypotheses was assessed using an information-theoretic approach and based on data from 1015 adult frogs obtained from 14 sites across the entire distributional range of the species. Results: There was strong evidence favouring a single model comprising temperature seasonality as the predictor variable. Larger body sizes were found in areas of greater seasonality, giving support to the "starvation resistance" hypothesis. Considering the known role of temperature on ectothermic metabolism, however, we formulated a new, non-exclusive hypothesis, termed "hibernation hypothesis": greater seasonality is expected to drive larger body size, since metabolic rate is reduced further and longer during colder, longer winters, leading to decreased energy depletion during hibernation, improved survival and increased longevity (and hence growth). Supporting this, a higher post-hibernation body condition in animals from areas of greater seasonality was found. Conclusions: Despite largely recognized effects of temperature on metabolic rate in ectotherms, its importance in determining body size in a gradient of seasonality has been largely overlooked so far. Based on our results, we present and discuss an alternative mechanism, the "hibernation hypothesis", underlying geographical body size variation, which can be helpful to improve our understanding of biogeographical patterns in ectotherms.https://frontiersinzoology.biomedcentral.com/articles/10.1186/s12983-015-0132-

Damped Seasonality Factors: Introduction

Previous research has shown that seasonal factors provide one of the most important ways to improve forecast accuracy. For example, in forecasts over an 18-month horizon for 68 monthly economic series from the M-Competition, Makridakis et al. (1984, Table 14) found that seasonal adjustments reduced the MAPE from 23.0 to 17.7 percent, an error reduction of 23%. On the other hand, research has also shown that seasonal factors sometimes increase forecast errors (e.g., Nelson, 1972). So, when forecasting with a data series measured in intervals that represent part of a year, should one use seasonal factors or not? Statistical tests have been devised to answer this question, and they have been quite useful. However, some people might say that the question is not fair. Why does it have to be either/or? Shouldn^Rt the question be ^Sto what extent should seasonal factors be used for a given series?^Tseasonal factors, forecast, accuracy

On the association between outdoor PM 2.5 concentration and the seasonality of tuberculosis for Beijing and Hong Kong

Tuberculosis (TB) is still a serious public health problem in various countries. One of the long-elusive but critical questions about TB is what the risk factors are and how they contribute for its seasonality. An ecologic study was conducted to examine the association between the variation of outdoor PM2.5 concentration and the TB seasonality based on the monthly TB notification and PM2.5 concentration data of Hong Kong and Beijing. Both descriptive analysis and Poisson regression analysis suggested that the outdoor PM2.5 concentration could be a potential risk factor for the seasonality of TB disease. The significant relationship between the number of TB cases and PM2.5 concentration was not changed when regression models were adjusted by sunshine duration, a potential confounder. The regression analysis showed that a 10 μg/m3 increase in PM2.5 concentrations during winter is significantly associated with a 3% (i.e. 18 and 14 cases for Beijing and Hong Kong, respectively) increase in the number of TB cases notified during the coming spring or summer for both Beijing and Hong Kong. Three potential mechanisms were proposed to explain the significant relationship: (1) increased PM2.5 exposure increases host's susceptibility to TB disease by impairing or modifying the immunology of the human respiratory system; (2) increased indoor activities during high outdoor PM2.5 episodes leads to an increase in human contact and thus the risk of TB transmission; (3) the seasonal change of PM2.5 concentration is correlated with the variation of other potential risk factors of TB seasonality. Preliminary evidence from the analysis of this work favors the first mechanism about the PM2.5 exposure-induced immunity impairment. This work adds new horizons to the explanation of the TB seasonality and improves our understanding of the potential mechanisms affecting TB incidence, which benefits the prevention and control of TB disease

Deterministic versus Stochastic Seasonal Fractional Integration and Structural Breaks

This paper considers a general model which allows for both deterministic and stochastic forms of seasonality, including fractional (stationary and nonstationary) orders of integration, and also incorporating endogenously determined structural breaks. Monte Carlo analysis shows that the suggested procedure performs well even in small samples, accurately capturing the seasonal properties of the series, and correctly detecting the break date. As an illustration, the model is estimated for four different US series (output, consumption, imports and exports). The results suggest that the seasonal patterns of these variables have changed over time: specifically, in the second subsample the systematic component of seasonality becomes insignificant, whilst the degree of persistence increases.deterministic and stochastic seasonality, fractional integration, structural breaks