Time Series Model Based Earning Forecasting

2013 ◽  
Vol 791-793 ◽  
pp. 2147-2150
Author(s):  
Xiang Rong Jiang ◽  
Ying Ying Cui
Keyword(s):  
Time Series ◽  
Empirical Study ◽  
Relative Stability ◽  
Listed Companies ◽  
Time Series Model ◽  
New Method ◽  
Time Series Models ◽  
Seasonal Patterns ◽  
Model Based ◽  
Over Time

We propose a procedure to forecast earning of listed companies. It is a modification of method developed for forecasting series with stable seasonal patterns. The new method is motivated by the observations that seasonal patterns, which may be evolving over time and remain relative stability, arise in finance market. The method can be applied to forecast individual observations as well as the end-of-season totals. Empirical study will be conducted with data from finance market to evaluate the performance of the proposed method. The new method is proved more effective than traditional time series models.

Seasonal drought forecasting in arid regions, using different time series models and RDI index

2019 ◽  
Vol 11 (3) ◽  
pp. 633-654 ◽  
Author(s):  
Mohammad Mehdi Moghimi ◽  
Abdol Rassoul Zarei ◽  
Mohammad Reza Mahmoudi
Keyword(s):  
Time Series ◽  
Ar Model ◽  
Time Series Models ◽  
Climatic Data ◽  
Drought Forecasting ◽  
Seasonal Drought ◽  
Drought Assessment ◽  
Model Based ◽  
Modeling And Forecasting ◽  
Drought Classes

Abstract Confronting drought and reducing its impacts requires modeling and forecasting of this phenomenon. In this research, the ability of different time series models (the ARIMA models with different structures) were evaluated to model and predict seasonal drought based on the RDI drought index in the south of Iran. For this purpose, the climatic data of 16 synoptic stations from 1980 to 2010 were used. Evaluation of time series models was based on trial and error. Results showed drought classes varied between ‘very wet’ to ‘severely dry’. The more occurrence frequency of ‘severely dry’ class compared to other drought classes represent the necessity of drought assessment and the importance of managing the effects of this phenomenon in the study area. Results showed that the highest severity of drought occurred at Abadeh, Shiraz, Fasa, Sirjan, Kerman, Shahre Babak and Saravan stations. According to selecting the best model fitted to the computed three-month RDI time series, results indicated that the MA model based on the Innovations method resulted in maximum cases with the best performance (37.5% of cases). The AR model based on the Yule–Walker method resulted in minimum cases with the best performance (6.3% of cases) in seasonal drought forecasting.

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