A population dynamical model of Operophtera brumata, L. extended by climatic factors

Setting out from the database of Operophtera brumata, L. in between 1973 and 2000 due to the Light Trap Network in Hungary, we introduce a simple theta-logistic population dynamical model based on endogenous and exogenous factors, only. We create an indicator set from which we can choose some elements with which we can improve the fitting results the most effectively. Than we extend the basic simple model with additive climatic factors. The parameter optimization is based on the minimized root mean square error. The best model is chosen according to the Akaike Information Criterion. Finally we run the calibrated extended model with daily outputs of the regional climate model RegCM3.1, regarding 1961-1990 as reference period and 2021-2050 with 2071-2100 as future predictions. The results of the three time intervals are fitted with Beta distributions and compared statistically. The expected changes are discussed

The possibilities of biodiversity monitoring based on Hungarian Light Trap Networks

Our method is presented with displaying time series, consisting of the daily amount of precipitation of 100 years, which has meant a separate challenge, as the precipitation data shows significant deviations. By nowadays, mankind has changed its environment to such an extent that it has a significant effect on other species as well. The Lepidoptera data series of the National Plant Protection and Forestry Light Trap Network can be used to justify this. This network has a national coverage, a large number of collected Lepidoptera, and an available, long data series of several years. For obtaining information from these data, the setting up of an easy to manage database is necessary. Furthermore, it is important to represent our data and our results in an easily analysable and expressive way. In this article the setting up of the database is introduced, together with the presentation of a three dimensional visualization method, which depicts the long-range and seasonal changes together

Duas propostas para solucionar o problema referente a médias móveis nulas na normalização adaptativa

This dissertation presents two new proposals to solve the problem of null moving averages for Adaptive Normalization, used as part of the pre-processing phase of a machine learning method for time series forecasting. They are: Adaptive Normalization by Compensation and Adaptive Normalization by Subtraction. It compares and analyzes the results obtained with these two new proposals, applied to 5 different datasets, with the Original Adaptive Normalization, as well as with other normalization methods present in the literature and the baseline ARIMA. It is concluded that, the Adaptive Normalization by Subtraction, proposed in this work, surpasses all other methods when it is applied to non-stationary heteroscedastic time series, also correcting the problem related to null moving averages of the Original Adaptive Normalization. For time series with high degree of stationarity, all the Adaptive Normalization methods are not satisfatory.Apresenta-se, nesta dissertação, duas novas propostas para sanar o problema das médias móveis nulas para a Normalização Adaptativa, utilizada como parte do pré-processamento de um método de aprendizagem de máquinas para previsão em séries temporais. São elas: Normalização Adaptativa Compensada e Normalização Adaptativa por Subtração. Compara-se e analisa-se os resultados obtidos com essas duas novas propostas, aplicadas a 5 diferentes Datasets, com a Normalização Adaptativa Original, assim como com outros métodos de normalização presentes na literatura e o baseline ARIMA. Conclui-se que, a Normalização Adaptativa por Subtração, proposta nesse trabalho, supera todos os outros métodos para séries temporais não-estacionárias e heteroscedásticas, também corrigindo o problema referente às médias móveis nulas para a Normalização Adaptativa Original. Para séries temporais com alto grau de estacionariedade, todos os métodos de Normalização Adaptativa não são satisfatórios

Influences of marketing response time on sales planning and forecasting in the industrial context

Thesis (D. Tech.(Marketing)) - Central University of Technology, Free state, 2012A reliable sales plan and forecast is the basis for good cash flow management and capacity planning. If the sales figures are below plan, the sales manager will increase the sales efforts in order to compensate these deviations. Usually, it can be expected that these efforts should be at least partly successful in the consumer markets. This situation is expected to be different in the industrial markets, as usually the generation of sales turnover can only be achieved by either new customers or new products sold to existing customers. It is therefore expected not to be possible to immediately compensate a loss of sales turnover within the planning period by increased sales efforts. This research project investigated whether industrial markets react differently from consumer markets by investigating the sales planning and forecasting process in the Machinery & Equipment Industry, the Automotive Supplier Tier 1 and the Automotive Supplier Tier 2 Industry. It investigated several time aspects of the sales process, displayed as customer-supplier interaction. The results of the research project showed that in fact sales processes in the investigated industry sectors have such a long duration, that it is not possible for sales managers to immediately compensate low sales figures by increased sales efforts. The sales turnover raise will come in a later period and thus simply too late for the current one. This results in the fact that the reliability of the sales forecast (for the established sales plan) is reduced, if industry characteristics and special time aspects of the sales process are not taken into consideration. These time aspects can be described best by the Market Response Time (MRT). The MRT is defined as the time lag between the start of an increase of sales efforts by the supplier (first contact) and the market response in terms of increased purchase. This is at the time when the customer starts to financially respond, with the result of a sales turnover increase at the supplier’s side. If the MRT is long, sales planning and forecasting has increased importance, because sales efforts need to be planned well in advance. For this reason response times are major elements in planning and forecasting, although it was previously not very well recognised in literature and practice. Based on a qualitative empirical study with the case study methodology, 41 case studies were undertaken within the three industry sectors. The investigated companies showed that these three industry sectors have different MRTs, such as 68 weeks in the Machinery & Equipment Industry, 138 weeks in the Automotive Supplier Tier 1, and 62 weeks in the Automotive Supplier Tier 2 Industry. These different MRTs influence the companies planning and forecasting processes in different ways. This research project qualitatively showed that if time aspects were taken into consideration in sales planning and forecasting, forecast accuracy could improve. It was furthermore indicated that an adequate sales planning approach could improve forecast accuracy as well. In a second step, it was indicated that these companies, which are aware of the time aspects, have shown a better sales performance in terms of sales force productivity, growth of productivity and market position. Concluding it can be stated that the respect of time aspects, such as MRT, may increase sales performance. The study's results have some limitations, which are the research context and the research methodology. As the project only investigated the industrial context, namely the Machinery & Equipment and the Automotive Tier 1 Supplier and Tier 2 Supplier Industry, its results can only be applicable to this context. The research methodology of this project is a qualitative one, which means that the sample size is small but deep and statistical generalisations cannot be made. Based on this, further research implications of this project are that its results may further be statistically generalised by quantitative studies. Especially the sales planning and forecasting processes in the detected clusters per industry sector should be investigated on a broad sample. Thirdly, the indicated relation between market knowledge and accuracy should be further investigated. This is because it can be estimated that the forecast accuracy is the highest if the company’s information horizon is equal to the product life cycle time of the products produced. Last of all, as there are only a few research projects done in the industrial context regarding market response models and time aspects, therefore these topics should be further investigated