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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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2004
In this chapter we will deal with classic, linear time series analysis. At first we will define the general linear process.
Jürgen Franke +2 more
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In this chapter we will deal with classic, linear time series analysis. At first we will define the general linear process.
Jürgen Franke +2 more
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Applied Mechanics and Materials, 2013
It is often the case that managers and social scientists are called to deal with time series. Time series analysis usually involves a study of the components of the time series and finding models that permit statistical inferences and predictions. ARIMA models are, in theory, the most general class of models for forecasting a time series.
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It is often the case that managers and social scientists are called to deal with time series. Time series analysis usually involves a study of the components of the time series and finding models that permit statistical inferences and predictions. ARIMA models are, in theory, the most general class of models for forecasting a time series.
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An enhanced ARIMA model for EEG classification
IEEE/WIC/ACM International Conference on Web Intelligence, 2021Yan Liu +5 more
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