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ON PREDICTION WITH FRACTIONALLY DIFFERENCED ARIMA MODELS
Journal of Time Series Analysis, 1988Abstract. This paper considers some extended results associated with the predictors of long‐memory time series models. These direct methods of obtaining predictors of fractionally differenced autoregressive integrated moving‐average (ARIMA) processes have advantages from the theoretical point of view.
Peiris, M. S, Perera, B. J. C
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Predictive Model of Births and Deaths with ARIMA
2020The paper show the application of the ARIMA (Autoregressive integrated moving average) prediction model is made, which consists of the use of statistical data (in this case, birth and deaths in Colombia) to formulate a system in which an approximation of future data is obtained, this thanks to the help of a statistical software that allows us to ...
Diana Janeth Lancheros Cuesta +3 more
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Management Science, 2005
This paper presents a multistage supply chain model that is based on Autoregressive Integrated Moving Average (ARIMA) time-series models. Given an ARIMA model of consumer demand and the lead times at each stage, it is shown that the orders and inventories at each stage are also ARIMA, and closed-form expressions for these models are given.
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This paper presents a multistage supply chain model that is based on Autoregressive Integrated Moving Average (ARIMA) time-series models. Given an ARIMA model of consumer demand and the lead times at each stage, it is shown that the orders and inventories at each stage are also ARIMA, and closed-form expressions for these models are given.
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International Journal of Forecasting, 1990
Abstract A number of empirical studies published in the forecasting literature in the 1970's and 1980's have come to the conclusion that univariate ARIMA time series modeling (Box-Jenkins) is not a more accurate univariate time series forecasting method than some simpler and older alternatives, including various exponential smoothing methods.
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Abstract A number of empirical studies published in the forecasting literature in the 1970's and 1980's have come to the conclusion that univariate ARIMA time series modeling (Box-Jenkins) is not a more accurate univariate time series forecasting method than some simpler and older alternatives, including various exponential smoothing methods.
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Forecasting Crime Using the ARIMA Model
2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008In this paper, time series model of ARIMA is used to make short-term forecasting of property crime for one city of China. With the given data of property crime for 50 weeks, an ARIMA model is determined and the crime amount of 1 week ahead is predicted. The modelpsilas fitting and forecasting results are compared with the SES and HES.
Peng Chen, Hongyong Yuan, Xueming Shu
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A DISTANCE MEASURE FOR CLASSIFYING ARIMA MODELS
Journal of Time Series Analysis, 1990Abstract. In a number of practical problems where clustering or choosing from a set of dynamic structures is needed, the introduction of a distance between the data is an early step in the application of multivariate statistical methods. In this paper a parametric approach is proposed in order to introduce a well‐defined metric on the class of ...
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Forecasting: Arima or Kalman Models
IFAC Proceedings Volumes, 1985Abstract In this article we have compared two of the currently most interesting quantitative models in forecasting applied to the socio-econcmic field, i.e. the ARIMA model and the Kalman filter. The comparison has been based on three fundamental points of view: model adequacy, identification procedure and forecasting function. We have identified two
J. Dekleva, N. Rožić
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An optimal k of kth MA-ARIMA models under a class of ARIMA model
Communications in Statistics - Theory and Methods, 2016ABSTRACTIn this article, we discuss finding the optimal k of (i) kth simple moving average, (ii) kth weighted moving average, and (iii) kth exponential weighted moving average based on simulated ARIMA(p, d, q) model. We run a simulation using the three above examining methods under specific conditions.
Dawoud I., Kaçiranlar S.
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ARIMA Forecasting Models in Inventory Control
Journal of the Operational Research Society, 1982In many industrial inventory control systems the policy of reordering and at what level depends crucially on the statistical properties of the random sum of a sequence of sales demands over the lead time. Current practice has conveniently assumed that the sales demands are independent.
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