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This paper aims to explore the effects generated by the Milan World Expo 2015 on both firm performance and seasonality structure. It aims to answer the following research question: Did the Milan Expo 2015 influence only hotel results without changing seasonal patterns, or was this mega event able to reconfigure seasonal periods?

The present analysis is based on Smith Travel Research (STR) data. This source offers daily data on a large sample of Milan hotels (approximately 80 per cent of the total), representing more than 30,000 rooms. The empirical data relate to a period of 12 years, 11 of which are focused on the pre-event period (2004-2014), while 2015 is centered on the Milan Expo. This data comprise 4,383 daily observations. For each day, three operating measures were analyzed: occupancy, average daily rate (ADR) and revenue per available room (RevPAR).

The empirical findings fully support the first hypothesis: the four seasonal periods built around the main market segments are relevant lenses for understanding Milan’s demand structure before Expo 2015. The findings also support the second hypothesis relating to the effects generated by the event: Expo 2015 was able to improve hotel performance during the four seasonal periods analyzed. The most fragile seasonality registered the highest rise. Finally, the last two hypotheses to be investigated are as follows: did the Milan Expo 2015 simply improve hotel performance, without changing the underlying seasonal patterns (H3), or did this event reconfigure the demand structure (H4)? The analyses carried out lend more support to the fourth hypothesis, suggesting that new seasonal patterns emerged during Expo 2015.

This paper explores the impact of a mega event on seasonal patterns of hotel performance metrics. At least three original aspects are introduced. First, to analyze the Milan demand variation, a market segment approach that proposes an innovative seasonal matrix is developed. This is based on the three main client groups attracted by the destination. Second, the effects generated by the Expo are measured with consideration given to the four seasonal periods. Third, based on graphical and statistical analysis, the paper confirms that new seasonal patterns emerged during the Expo.

This paper examines the empirical patterns of futures prices volatility by using different seasonal adjustment techniques The average absolute month to month percentage (AAPC) figures are used to describe the extent of smoothness when seasonal adjustment methods are applied. Several interesting patterns are suggested from the observation of different futures contracts. The authors then suggest further that if seasonal patterns do exist for futures prices volatility, it is possible to focus the study of futures prices volatility on the different seasonal filters selection, and/or on the different seasonal models alternatives.

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.

Develops a method for estimating the monthly milk price schedule needed to counter the effects of seasonality, which is an enduring feature of milk production in the UK. The issue of seasonality has been mostly ignored in studies estimating milk supply functions. In this paper milk supply functions which explicitly take account of seasonality are estimated for Northern Ireland and Scotland. Pre‐testing of monthly milk price and milk supply time‐series, using an extended HEGY test and an ADF test, indicated the presence of deterministic seasonality. Empirical milk supply models incorporating seasonal dummy variables to account for deterministic seasonality were estimated in the two regions of study. The results of these models were used to calculate the monthly producer milk price schedule required to encourage dairy farmers to produce an even monthly milk supply pattern. These calculations indicated that, in the long run, a peak‐to‐trough seasonal price differential of around 8 pence per litre would be required to produce an even pattern of milk supply in Scotland, and 11 pence per litre would be required in Northern Ireland.

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.

The purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to propose a parallel-series hybridization of seasonal neural networks and statistical models for demand time series forecasting.

The main idea of proposed model is centered around combining parallel and series hybrid methodologies to use the benefit of unique advantages of both hybrid strategies as well as intelligent and classic seasonal time series models simultaneously for achieving results that are more accurate for the first time. In the proposed model, in contrast of traditional parallel and series hybrid strategies, it can be generally shown that the performance of the proposed model will not be worse than components.

Empirical results of forecasting two well-known seasonal time series data sets, including the total production value of the Taiwan machinery industry and the sales volume of soft drinks, indicate that the proposed model can effectively improve the forecasting accuracy achieved by either of their components used in isolation. In addition, the proposed model can achieve more accurate results than parallel and series hybrid model with same components. Therefore, the proposed model can be used as an appropriate alternative model for seasonal time series forecasting, especially when higher forecasting accuracy is needed.

To the best of the authors’ knowledge, the proposed model, for first time and in contrast of traditional parallel and series hybrid strategies, is developed.

This paper aims to deal with the construction of seasonal price indices for the housing market, based on Rosen's hedonic equations and using spatial econometric autoregression (SAR) techniques.

More precisely, the hedonic equations are estimated using disaggregated data, and the extracted indices are averaged over zip code areas. Then the seasonality, which is considered deterministic, is extracted after eliminating the spatial effects. The data set used consists of 8,685 valuations of dwellings, detached dwellings and detached houses that took place in Attica on behalf of a commercial bank during the period 2000‐2009.

The paper concludes that evidence exists to support the hypothesis that property prices are affected by seasonal and spatial effects beyond structural effects and the effects of the general economic situation. Property valuations are strongly connected with deterministic exogenous variables, such as the size, age and location of the property, the general economic situation, and to a lesser effect the spatial system and the season during which the valuation took place. The estimated spatial effect is positive and quite large in value, indicating a landscape consisting of large homogeneous sub‐areas, while the results demonstrate a seasonal upturn during the first semester and downturn towards the end of the year.

This paper provides a framework for incorporating spatial and seasonal effects in property price index construction.

In this article, we propose a new class of flexible seasonal time series models to characterize the trend and seasonal variations. The proposed model consists of a common trend function over periods and additive individual trend (seasonal effect) functions that are specific to each season within periods. A local linear approach is developed to estimate the trend and seasonal effect functions. The consistency and asymptotic normality of the proposed estimators, together with a consistent estimator of the asymptotic variance, are obtained under the α-mixing conditions and without specifying the error distribution. The proposed methodologies are illustrated with a simulated example and two economic and financial time series, which exhibit nonlinear and nonstationary behavior.

The relationship between electricity demand and weather in the United States has been studied as of late due to increased demand, de-regulation, and new pricing models. The influence of weather or seasonality in energy consumption, particularly electricity demand, has been widely researched. A significant scientific interest in the seasonality of energy consumption has led to an important number of papers exploring the role of weather variability and change on energy consumption. Most of these papers model demand as a function of seasonal climate factors.

The goal of this research is a broad examination of monthly residential electricity demand for a region of the mid-Atlantic using Excel and step-wise regression. This is achieved by using a sequence of models built in Excel in which different patterns are gradually introduced in the estimations. Data over a seven-year period is utilized. A backward elimination step-wise regression analysis is employed to determine which independent variables best model the data. Initial independent variables included high monthly temperature, low monthly temperature, time, year, month, seasonal quarter, and introduction of a “green” tax credit for solar and wind energy.

Models for forecasting the electricity demand and the predictive power of these models is assessed. The work is organized as follows: Data description and the methodology, trend and the seasonality of electricity usage in the mid-Atlantic region, the predictive power and seasonality of the models, and main conclusions drawn from the study.

Mechanisms to adjust planned lead times based on current work loads are desirable for time‐phased planning systems. This paper investigates the use of exponentially smoothed order flow time feedback in setting planned lead times dynamically. The system studied is a supply chain with capacity‐constrained processing stations and transit times between stations. Lot sizes are based on the minimization of flow times using queuing approximations. Both seasonal and level demand patterns with uncertainty are considered. Since both dependent and independent demands are assumed at each station, customer delivery performance depends on the distribution of inventory along the supply chain. Results show that dynamic planned lead time setting can be used effectively to control delivery performance along the supply chain. Performance is also influenced significantly by appropriate lot size selection.