scholarly journals Constructing Demand and Supply Forecasting Model of Social Service using Time Series Analysis : Focusing on the Development Rehabilitation Service

2015 ◽  
Vol 15 (6) ◽  
pp. 399-410
Author(s):  
Jeong-Min Seo
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Social Service ◽  
Rehabilitation Service ◽  
Forecasting Model ◽  
Demand And Supply ◽  
Series Analysis

A Time Series Analysis and Forecasting Model in Dam Safety Monitoring

2013 ◽  
Vol 756-759 ◽  
pp. 589-593
Author(s):  
Rui Lian Hou ◽  
Hu Gao ◽  
Hui Li
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Safety Monitoring ◽  
Measured Data ◽  
Forecasting Model ◽  
Dam Safety ◽  
Model Structure ◽  
Series Analysis ◽  
Dam Safety Monitoring ◽  
Forecast Data

Based on the new time series analysis and the theory of dam safety, this paper proposed a new forecasting model in dam safety monitoring. First the paper introduced basic method of flat forecast and described its algorithm routine, and explained what is time series. Second the model structure of monitoring system based on time series analysis was given through the analysis of the method. The system can provide the comparison chart of the measured data, forecast data and trends. At last the model was realized based on VB and tested by the actual data. Experimental results show that this forecasting model has better prediction results in dam safety monitoring.

A Study on Surface Evaporation in Liaoyang Based on Grey Theory

2014 ◽  
Vol 675-677 ◽  
pp. 875-879
Author(s):  
Jin Liang Chen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Climate Models ◽  
Visual Basic ◽  
Liaoning Province ◽  
Forecasting Model ◽  
Grey Theory ◽  
Surface Evaporation ◽  
Series Analysis ◽  
Grey Forecasting

At present, there are mainly two methods to forecast the quantity of surface evaporation: one is by time series analysis, another is by climate models. This paper established models to simulate the surface evaporation in Liaoyang based on grey theory, and developed a grey forecasting software for surface evaporation using Visual Basic. From the annual depreciation from 1998 to 2010 in Liaoyang station in Liaoning Province, a grey forecasting model was established, which was then used to predict the quantity of surface evaporation in Liaoyang from 2011 to 2015.

Forecasting of Production and Consumption of Ethanol in Thailand Using Time Series Analysis

2015 ◽  
Vol 781 ◽  
pp. 651-654
Author(s):  
Sasiwimon Sriyotha ◽  
Rojanee Homchalee ◽  
Weerapat Sessomboon
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Ethanol Production ◽  
Agricultural Products ◽  
Percentage Error ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Series Analysis ◽  
Production And Consumption ◽  
The Mean

Ethanol is the important renewable energy in Thailand. It is alcohol produced from sugarcane and tapioca that are agricultural products available in Thailand. Ethanol is used to blend with gasoline for use as gasohol. Ethanol production and consumption in Thailand are fluctuating. Consequently, planning of ethanol production and consumption is irrelevant. In order to solve this problem, this study aims to find forecasting models using time series analysis including exponential smoothing and the Box-Jenkins methods. The most appropriate forecasting model was selected from the two methods by considering the minimum of the mean absolute percentage error: MAPE. It was found that the Box-Jenkins is the most appropriate method to forecast both ethanol production and consumption. The forecasting results were then used to determine appropriate quantity and proportion of molasses and tapioca needed for ethanol production in the future.

scholarly journals Construction and Simulation of Economic Statistics Measurement Model Based on Time Series Analysis and Forecast

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lu Xu ◽  
Weijie Chen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Forecasting Model ◽  
Basic Principles ◽  
Series Analysis ◽  
Model Based ◽  
Combined Forecasting

Time series follow the basic principles of mathematical statistics and can provide a set of scientifically based dynamic data processing methods. Using this method, various types of data can be approximated by corresponding mathematical models, and then, the internal structure and complex characteristics of the data can be understood essentially, so as to achieve the purpose of predicting its development trend. This paper mainly studies the combined forecasting model based on the time series model and its application. First, the application prospects and research status of the combined forecasting model, the source of time series analysis, and the status of research development at home and abroad are given, and the purpose and significance of the research topic and the research content are summarized. Then, the paper gives the relevant theories about the ARIMA model and the basic principles of model recognition and explains the method of time series smoothing. Finally, the paper uses the ARIMA model to identify and fit the time series data and then the gray forecast model to fit and predict the time series data. Finally, by assigning reasonable weights and combining these methods, a combined forecasting model is proposed and carried out.

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