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The global financial crisis (GFC) of 2008‐2009 has highlighted the need for understanding fluctuations in housing variables and how, as such, they contribute to understanding how housing markets work. The contention of this paper is to present a univariate structural time series analysis of the Australian Housing Finance Commitments (HFCs) covering the period 1988:6‐2009:5. The empirical analysis aims to focus on establishing whether monthly HFCs exhibit the expected cyclical and seasonal variations. The presence of a monthly seasonal pattern in HFCs is to be ascertained by way of testing possible hypotheses that explain such a pattern.

A structural time series framework approach, used in this paper, is in line with that promulgated by Harvey. Such models can be interpreted as regressions on functions of time in which the parameters are time‐varying. This makes them a natural vehicle for handling changing seasonality of a complex form. The structural time series model is applied to seasonally unadjusted monthly HFCs, between 1988:6 and 2009:5. The data have been sourced from the ABS. For consistency, the sample for each variable is standardised to start with the first available July observation and end with the latest available June observation.

The modelling results confirm the presence of cyclicality in HFCs. The magnitude of the observed cycle‐related changes is A$817m. A structural time series model incorporating trigonometric specification reveals that seasonality is also present and that it is stochastic (as implied by the inconsistency of the monthly seasonal factors over the sample period). The magnitude of monthly seasonal changes is A$435.8m. The results show the presence of statistically significant factors for January, February, March, April, May, September, October and November, which are attributed to “spring”, “summer” and “autumn” seasonal effects.

Empirical evidence of variations in housing‐related variables is relatively limited. A study of the literature uncovered that most studies focus on house prices and found no empirical research focusing on fluctuations in HFCs. Consequently, this research aims to be the first to explain the presence of seasonal and cyclical fluctuations in such an important housing variable as HFCs. Moreover, the paper aims to enhance the practice of modelling seasonal influences on housing variables.

The composition and properties of milk are considerably important for dairy farmers, manufacturers and consumers. Despite the significant role of bovine milk in Iranian dairy products, there is little information about the effect of production season and location on the physicochemical properties of pasteurized milk as a final product. The purpose of this study was to investigate the effects of seasonal, geographical and product brand variations on the chemical components and physical properties of Iranian pasteurized bovine milk.

A total of 400 samples of pasteurized milk were obtained during a 12-month period, from April 2014 to March 2015, using random sampling. Chemical components (protein, fat, lactose, dry matter and solids-not-fat) and physical properties (freezing point, extraneous water content, titratable acidity, density and pH) of the collected samples were analyzed. A one-way ANOVA was used to perform the statistical analysis of data, and results were presented as the mean ± standard deviation.

It was found that the biochemical constituents and physical properties of pasteurized milk samples were linked to seasonal and geographical variation parameters. The milk sampled during spring and summer contained significantly less fat, protein and solids-not-fat (p < 0.05) than samples in autumn and winter. Also, samples in spring had a significantly higher (p < 0.05) extraneous water (0.8 per cent) compared to milk sampled in winter (−0.4 per cent). Samples in Maragheh and Mianeh contained the highest level of fat (2.82 per cent) and protein (3.09 per cent) content in the province. The sampled milk from the south (Mianeh and Hashtrud) and the northwest (Marand) had also significantly higher (p < 0.01) freezing points than the other areas. No significant differences (p > 0.01) were found in physicochemical properties in different product brands of the milk samples.

Seasonal and geographical parameters are crucial factors in the diversity of physicochemical parameters of commercial pasteurized milk. In this study, unlike the other studies, differences in milk product brand were not significant. Further research will be needed to assess other factors such as the effect of management practices and feeding strategies on farms.

The purpose of this paper is to assess the inter-seasonal temperature and precipitation changes in Setif high plains region under future greenhouse gas emissions, by using four general circulation models (GCMs) output data between three time slices of twenty-first century. The objective is to show the vulnerability of the region and the strategy of adaptation to these changes.

This study investigates likely changes in seasonal temperature and precipitation over Setif high plains region (North East of Algeria) between three time slices: 2025, 2050 and 2075. The projections are based on the SRES A2 and B2 scenarios. MAGICC-SCENGEN 5.3v.2 was used as a tool for downscaling the four selected GCMs output data. The vulnerability of the region, coupled with the possible impacts climate change, stresses the need for adaptive strategies in key sectors in the region for the long term sustainable development.

The results for change in seasonal temperature indicate a general warming under the two scenarios till the year 2075.The results of GFDLCM21 and GFDLCM20 show a general reduction of spring and autumn precipitations and an increase in winter and summer. BCCRBCM2 predicts a decrease in winter, spring and summer precipitations and an increase in autumn. Climate change, as well as increases in climate variability, will alter precipitation, temperature and evaporation regimes, and will increase the vulnerability of Setif high plains to changes in hydrological cycles. Climate and weather forecasting coupled with biotechnological advances in improving crop yields and tolerances to aridity, is likely to bring significant payoffs for strategy of adaptation in the field of agricultural water management.

This work is one of the first to study inter-seasonal temperature and precipitation changes under global warming over the region, and suggest some adaptive strategies to limit the effect of these changes.

This paper attempts to examine a number of issues regarding the returns on the Hang Seng Index Futures traded in Hong Kong. The daily returns are separated into close‐to‐close, close‐to‐open, and open‐to‐close periods and the three returns examined for autocorrelation, GARCH and seasonal effects. The study reveals that the CLCL returns are autocorrelated and that most of the returns exhibit GARCH effects. With regard to seasonal effects the results are mixed.

Pakistan is an energy starving country that needs continuous supply of energy to keep up its economic speed. The aim of this paper is to assess the wind resource and energy potential of Quaidabad site for minimizing the dependence on fuels and improving the environment.

The Quaidabad site wind shear coefficient and turbulence intensity factor are investigated. The two-parameter k and c Weibull distribution function is used to analyze the wind speed of site. The standard deviation of the site is also assessed for a period of a year. The wind power density and energy density are assessed for a period of a year. The economic assessment of energy/kWh is investigated for selection of appropriate wind turbine.

The mean wind shear coefficient was observed to be 0.2719, 0.2191 and 0.1698 at 20, 40 and 60 m, respectively, for a period of a year. The mean wind speed is found to be 2.961, 3.563, 3.907 and 4.099 m/s at 20, 40, 60 and 80 m, respectively. The mean values of k parameters were observed to be 1.563, 2.092, 2.434 and 2.576 at 20, 40, 60 and 80 m, respectively, for a period of a year. The mean values of c m/s parameter were found to be 3.341, 4.020, 4.408 and 4.625 m/s at 20, 40, 60 and 80 m, respectively, for a period of a year. The major portion of values of standard deviation was found to be in between 0.1 and 2.00 at 20, 40, 60 and 80 m. The wind power density (W/m²) sum total values were observed to be 351, 597, 792 and 923 W/m² at 20, 40, 60 and 80 m, respectively, for a period of a year. The mean coefficient of variation was found to be 0.161, 0.130, 0.115 and 0.105 at 20, 40, 60 and 80 m, respectively. The sum total energy density was observed to be 1,157, 2,156, 2,970 and 3,778 kWh/m² at 20, 40, 60 and 80 m, respectively. The economic assessment is showing that wind turbine E has the minimum cost US$0.049/kWh.

The Quaidabad site is suitable for installing the utility wind turbines for energy generation at the lowest cost.

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.

The subject of this paper is seasonal variation in the return on stocks. The phenomenon we analyze here is known as the “Halloween effect” or the trading strategy “sell in May and go away.” The authors test the hypothesis that stock markets tend to return considerably less in the six months beginning in May than in the other half of the year. This effect has shown persistency over time and is seemingly large enough to be a candidate for economic significance.

The authors analyze monthly data from 13 countries for the period 1958–2019, using the Kruskal–Wallis test, t-test and a boot-strap based estimator. In addition, we look a sub-periods for a larger group of countries and include data on both stock returns and interest rates.

The authors find a strong seasonal effect in a large majority of the markets, with the period from November to April seeing higher returns than the other six months of the year. This result also holds for a larger sample of countries based on data from a shorter period. The effect is found to be economically significant in most countries in the sample. The authors examine one potential explanation for seasonal variation in stock returns, i.e. seasonal affective disorder (SAD). The authors find some, albeit weak, support for this hypothesis.

This paper uses a rich dataset that has not been used for this purpose before and robust tests of statistical and economic significance to shed light on an important aspect of global financial markets.

Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.

This paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the GM(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal GM(1,1) model (SAGM(1,1)), the self-adaptive seasonal discrete grey model (SADGM(1,1)) and the self-adaptive seasonal fractional-order grey model (SAFGM(1,1)), are put forward for prognosticating the air quality of all provinces in China .

The experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the SARIMA model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.

Since air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.

Given the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.

The self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the SARIMA model.

This paper aims to examine whether greater consideration should be given to the timing of sexual health interventions within the calendar year.

The paper uses a review of the literature.

The evidence points to seasonality in a number of areas of sexual health among young people, including: the timing of first intercourse and conceptions, both of which peak in the summer and over Christmas; abortions which peak approximately two months later in February and late summer and sexually transmitted infections, which peak over the summer and autumn. In the case of conceptions there is evidence that the seasonal pattern among young people is different from that of adults. Potential explanations fall into four main categories: biological; behavioural; social, and service‐related.

Many of the studies included in this review are from the USA, and some are based on either small samples or specific risk groups, which raises questions of representativeness and generalisability. Further, it is notable how little research there has been regarding seasonal variations in other aspects of sexual behaviour, such as risk reduction practice and other potential explanatory factors such as health‐seeking behaviour and availability of services.

The findings consistently point to periods of heightened sexual activity among young people in the summer and over Christmas, and suggest that greater consideration should indeed be given to the timing of sexual health interventions within the calendar year.

To the best of the authors' knowledge, no other review of this kind has yet been found.

Adolescent aggressive behavior has generated concern about/increasing rates of youth violence in schools. It is important to perform new research using different methods and approximations to obtain a better understanding of this multifactorial phenomenon. A poorly studied area consists of the presence of seasonal differences in adolescent aggressive behavior. Accordingly, several studies (with contradictory results) have found that adult aggressive behavior varies according to seasonality. The purpose of this paper is to use observational descriptive methods to analyze, during different seasons, adolescent aggressive behavior among students in schools of Santiago de Chile.

In all, 32 aggressive interactions between dyads of male adolescents (14-18 years) were recorded using observational methods (i.e. ethological methodology) in a complete academic class in two schools from Santiago de Chile. Subsequently, the paper constructed intensity aggressive indexes based on behavioral data.

The first contact, initiating aggressive interaction, and the aggression frequency were higher during warm season (i.e. spring) rather than cold season (autumn-winter). The aggression intensity of the complete interaction was higher during cold season. In addition, temperature was negatively associated to aggression intensity.

These results, apparently contradictory, can serve to support classic models used to explain seasonal differences in aggressiveness, where the intensity of the first aggression could be the mediator of aggressiveness intensity in the interaction. Finally, the paper proposes that seasonal differences must be taken into account as an impact factor over the frequency of adolescent male aggression in schools.