scholarly journals Comparison of daily flows simulated for the year 2060 on the Kaczawa River for various scenarios of climate change by simple time series analysis

2019 ◽  
Vol 100 ◽  
pp. 00041
Author(s):  
Leszek Kuchar ◽  
Ewa Broszkiewicz-Suwaj ◽  
Slawomir Iwanski ◽  
Leszek Jelonek
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Analysis ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Change Scenario ◽  
Series Analysis ◽  
Daily Data ◽  
Data Generator ◽  
Odra River

In this paper a time series analysis for daily flow simulations according three climate change scenario for Kaczawa River a left side tributary of the Odra River in south-west Poland is presented. The flow sequences were simulated using the hydrological model MIKE SHE and the spatial SWGEN meteorological data generator. Meteorological data for the hydrological model were generated based on data from 24 meteorological stations and 35-year daily data from the Institute of Meteorology and Water Management of the National Research Institute (IMGW). Data were generated for future climate condition for 2060 according GISS Model E, HadCM3, and GFDL R15 scenarios as well for the present conditions. The year 2000 was used as a reference year. The results obtained on the basis of a simple time series analysis point to small changes in flows for current and simulated conditions for 2060 for the Kaczawa River.

Climate change impacts on Indian Sunderbans: a time series analysis (1924–2008)

2012 ◽  
Vol 21 (5) ◽  
pp. 1289-1307 ◽  
Author(s):  
Atanu Raha ◽  
Susmita Das ◽  
Kakoli Banerjee ◽  
Abhijit Mitra
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Analysis ◽  
Climate Change Impacts ◽  
Series Analysis

scholarly journals Forecasting the Primary Demand for a Beer Brand Using Time Series Analysis

Organizacija ◽  
2008 ◽  
Vol 41 (3) ◽  
pp. 116-124 ◽  
Author(s):  
Danijel Bratina ◽  
Armand Faganel
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Discrete Event ◽  
Marketing Research ◽  
Causality Analysis ◽  
Sales Promotions ◽  
Current Application ◽  
Series Analysis ◽  
Daily Data ◽  
The Impact

Forecasting the Primary Demand for a Beer Brand Using Time Series AnalysisMarket research often uses data (i.e. marketing mix variables) that is equally spaced over time. Time series theory is perfectly suited to study this phenomena's dependency on time. It is used for forecasting and causality analysis, but their greatest strength is in studying the impact of a discrete event in time, which makes it a powerful tool for marketers. This article introduces the basic concepts behind time series theory and illustrates its current application in marketing research. We use time series analysis to forecast the demand for beer on the Slovenian market using scanner data from two major retail stores. Before our analysis, only broader time spans have been used to perform time series analysis (weekly, monthly, quarterly or yearly data). In our study we analyse daily data, which is supposed to carry a lot of ‘noise’. We show that - even with noise carrying data - a better model can be computed using time series forecasting, explaining much more variance compared to regular regression. Our analysis also confirms the effect of short term sales promotions on beer demand, which is in conformity with other studies in this field.

Introduction to the special issue on “hydro-meteorological time series analysis and their relation to climate change”

2018 ◽  
Vol 66 (3) ◽  
pp. 317-318 ◽  
Author(s):  
Javier Estévez ◽  
Amanda García Marín ◽  
Julián Báez Benitez ◽  
M. Carmen Casas Castillo ◽  
Luciano Telesca
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Analysis ◽  
Special Issue ◽  
Series Analysis
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