1) component of time series 时间序列成分 2) Major factors analysis involved in time series 时间序列主成份分析 3) Metallogenetic Time Series 成矿时间序列 4) time series analysis 时间序列分析 1. Application oftime series analysisin the prediction of schistosomiasis prevalence in the areas of “brea...
The trend component of time series.Xiaobing YangQionghong DuanJianjie WangZhengbin ZhangGaofeng Jiang
This paper considers how ARCH effects may be handled in time series models formulated in terms of unobserved components. A general model is formulated, but this includes as special cases a random walk plus noise model with both disturbances subject to ARCH effects, an ARCH-M model with a time...
It is generally acknowledged that the growth rate of output, the seasonal pattern, and the business cycle are best estimated simultaneously. To achieve this, we develop an unobserved component time series model for seasonally unadjusted US GDP. Our model incorporates a Markov switching regime to pro...
求翻译:for each of the four time series components(trend, cyclic, seasonal and irregular) state whether the component is present, and explain why you believe this is so.是什么意思?待解决 悬赏分:1 - 离问题结束还有 for each of the four time series components(trend, cyclic, seasonal and ...
Patterns in temporal data are often across different scales, such as days, weeks, and months, making effective visualization of time-based data challenging.Periphery Plotsare a new approach for providing focus and context in time-based charts to enable interpretation of patterns across heterogeneous ...
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An example of time series for meteorological data and some comparative results between the techniques under study are given. Different methods of ordering ICs are also presented, including a new one, which may influence the quality of the reconstruction of the original data. 展开 ...
As financial time series are inherently noisy and non-stationary, it is regarded as one of the most challenging applications of time series forecasting. Due to the advantages of generalization capability in obtaining a unique solution, support vector regression (SVR) has also been successfully applied...
In addition to the autoregressive models described above, which are used for instance in the form of GARCH models when modeling volatility, a further technique of time series analysis, called principal component analysis (abbreviated as PCA), is widely applied in the financial world.This...