Trends In Historical Data Research Articles

Impact of climate change on runoff in the upper part of the Euphrates basin

Among the processes most affected by global warming are the hydrological cycle and water resources. Regions where the majority of runoff consists of snowmelt are very sensitive to climate change. It is significant to express the relationship between climate change and snow hydrology and it is imperative to perform climate change impact studies on snow hydrology at global and regional scales. Climate change impacts on the mountainous Upper Euphrates Basin were investigated in this paper. First, historical data trend analysis of significant hydro-meteorological data is presented. Available future climate data are then explained, and, finally, future climate data are used in hydrological models, which are calibrated and validated using historical hydro-meteorological data, and future streamflow is projected for the period 2070–2100. The hydrological model outcomes indicate substantial runoff decreases in summer and spring season runoff, which will have significant consequences on water sectors in the Euphrates Basin. Citation Yilmaz, A.G. & Imteaz, M.A. (2011) Impact of climate change on runoff in the upper part of the Euphrates basin. Hydrol. Sci. J. 56(7), 1265–1279.

Pattern Matching Fuzzy Models for Time series Forecasting with Application

We study a new technique of forecasting time series , this is technique is pattern matching Fuzzy, which identify past relationships and its trends in historical data for forecasting future values .We connect Fuzzy Pattern Matching model with time series and we also make algorithm of Fuzzy Pattern Matching and application it on the real data (consuming electric energy in Ninevah Governerate) , and we used Mean Squared Deviation (MSD) and Mean Absolute Deviation (MAD) to get the optimal values

NAFTA, Infrastructure and the Canadian Automotive Sector

The paper examines recent trends such as just-in-time production, outsourcing, and the re-organization of the North American automotive sector, as well as projected developments in Asia, especially in Japan and China, and their impacts on the Canadian auto sector. In addition to presenting trends in historical data on the Canadian automotive sector, a CES-translog cost model is estimated and analyzed to study developments in the sector.

Accessing politeness axes: forms of address and terms of reference in early English correspondence

There are certain areas of study where present-day pragmatics can learn from history. This article focuses on the socio-pragmatic aspects of forms of address and terms of reference in late 16th-century English correspondence. The aim of the study is to explore the extent to which the use of forms of address and reference, and the factors which influence their choice, can be seen to have any general trends. A further goal is to relate these trends in historical data to such contemporary views as Brown and Levinson’s [Politeness: Some Universals in Language Usage, Cambridge University Press, Cambridge, 1987] politeness theory, Comrie’s [Linguistic politeness axes: speaker‐addressee, speaker‐referent, speaker‐bystander. Pragmatics Microfiche 1.7: A3, Department of Linguistics, University of Cambridge, 1976] politeness axes, and Bell’s [Language style as audience design. Language in Society 13, 145‐204] audience design. The material itself, the Corpus of Early English Correspondence (CEEC), gives a unique opportunity to explore the influence of factors like relative power and social distance on the use of forms of address and reference in the highly stratified society of the Renaissance period. The study shows that referential terms are often derived from the range of direct address formulae. In direct address, when the social status of either the addressee or the referent is very high, it seems to override the influence of social distance. In reference, the reasons for the choice of an appropriate term are more complex, and the parameters set for, e.g., positive and negative politeness can no longer be seen as equally valid. # 2004 Elsevier B.V. All rights reserved.

PATTERN MODELLING IN TIME-SERIES FORECASTING

Pattern modelling in time-series prediction refers to the process of identifying past relationships and trends in historical data for predicting future values. This paper describes the development of a new pattern-matching technique for univariate time-series forecasting. The pattern modelling technique outperforms frequently used statistical methods such as exponential smoothing on different error measures and predicting the direction of change in time-series. The paper discusses the prediction results on popular benchmarks and the real U.S. S&P index for financial markets.

Projecting future drug expenditures—1992

The effects of inflation, generic competition, and the introduction of new drug entities on future drug expenditures are discussed. The impact of inflation can be predicted by analyzing trends in historical data and determining factors that may affect drug use. Useful sources include U.S. Bureau of Labor Statistics Producer Price Index figures for pharmaceuticals, commercially available data on drug product pricing, and market basket indices compiled by the Health Care Financing Administration (HCFA) and the American Hospital Association. HCFA predicts that quarterly increases in pharmaceutical prices in 1992-93 will range from 6.4% to 8.1%. Pharmaceutical industry analysts predict overall annual inflation rates for pharmaceuticals in 1991-92 ranging from 8% to 10%. To evaluate generic competition, information on the expiration of patents and market exclusivity can be used. Decreases of 30-70% in the list price of a drug product can be expected after the introduction of a generic product, and the price usually stabilizes at approximately 50% of the list price of the innovator product at the time the first generic product was marketed. Predicting when new drug entities will be introduced is difficult; analysts say the usual time from filing of a new drug application to FDA approval is two years. The FDA says approval usually follows within a few weeks after an advisory panel's recommendation for approval, and marketing can be expected within a few months after final FDA approval. Assumptions about future drug expenditures should take into account historical and predicted rates of inflation, the status of generic competition for existing products, and the prospects for introduction of new drug entities.(ABSTRACT TRUNCATED AT 250 WORDS)