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识别时间序列中的简化结构AI 解读

This article studies how to identify hidden factors in multivariate time series process. This problem is important because, when the series are driven by a set of common factors, (a) a large number of parameters may be needed to obtain an adequate representation of the system and (b) the estimated parameters will be highly correlated. Therefore, a complex and badly defined relationship can appear when, in fact, a simpler and parsimonious model in terms of a few common…

季节时间序列的趋势与增长率估计AI 解读

We consider the problem of trend estimation from an ARIMA-model-based perspective. Given that the time series of interest is well represented by a multiplicative seasonal ARIMA model (Box and Jenkins 1970), a method is presented for estimating the trend of this series as a component of the model's forecast function. This method is applied to the “airline model,” a commonly occurring model form for economic and social time series. For those numerous series that are rendered…

季节性时间序列的趋势与增长率估计AI 解读

We consider the problem of trend estimation from an ARIMA-model-based perspective. Given that the time series of interest is well represented by a multiplicative seasonal ARIMA model (Box and Jenkins 1970), a method is presented for estimating the trend of this series as a component of the model's forecast function. This method is applied to the “airline model,” a commonly occurring model form for economic and social time series. For those numerous series that are rendered…

平稳自回归移动平均模型的似然函数AI 解读

This paper examines the likelihood function of a stationary autoregressive-moving average model of order (p, q) and presents a method for its evaluation. Numerical results illustrating the computational efficiency of the method are given.

时间序列中若干异常值问题的贝叶斯分析AI 解读

Two models, the aberrant innovation model and the aberrant observation model, are considered to characterize outliers in time series. The approach adopted here allows for a small probability α that any given observation is ‘bad’ and in this set-up the inference about the parameters of an autoregressive model is considered.