青海省位于中国西部,拥有丰富的草地资源和独特的畜牧业生产条件,使得该地区的肉类产量在全国占有重要地位。肉类产量不仅直接影响当地牧民的收入水平,还关系到青海省的经济发展和社会稳定。有鉴于此,采用R studio软件建立自回归差分移...青海省位于中国西部,拥有丰富的草地资源和独特的畜牧业生产条件,使得该地区的肉类产量在全国占有重要地位。肉类产量不仅直接影响当地牧民的收入水平,还关系到青海省的经济发展和社会稳定。有鉴于此,采用R studio软件建立自回归差分移动平均(ARIMA)模型可对青海省的肉类产量进行历史数据分析和未来趋势预测,并利用AIC准则确定模型的最优阶数。本文使用1997~2020年的青海省肉类产量作为数据源,在此基础上采用2021~2023年青海省肉类产量数据作为对比数据来判断真实值与预测值之间的差异,最终可得出其真实值与预测值之前有一定差异,但差异较小,整体预测精度较高。Qinghai Province, located in western China, has rich grassland resources and unique livestock production conditions, which make the meat production in the region occupy an important position in China. Meat production not only directly affects the income level of local herders, but also relates to the economic development and social stability of Qinghai Province. In view of this, the autoregressive integrated moving average (ARIMA) model can be built using R studio software to analyze the historical data and forecast the future trend of meat production in Qinghai Province, and the optimal order of the model can be determined using the AIC criterion. In this paper, the meat production of Qinghai Province from 1997 to 2020 is used as the data source, and on this basis, the meat production data of Qinghai Province from 2021 to 2023 is used as the comparative data to judge the difference between the real value and the forecast value, and finally, it can be concluded that there is a certain difference between the real value and the forecast value, but the difference is small, and the overall forecast accuracy is high.展开更多
Based on the analysis of periodogram, this paper discussed the properties of periodogram of autoregressive integrated moving average process ARIMA（p,1 ,q）, and found that the periodgram of ARIMA（p,1 ,q） process is...Based on the analysis of periodogram, this paper discussed the properties of periodogram of autoregressive integrated moving average process ARIMA（p,1 ,q）, and found that the periodgram of ARIMA（p,1 ,q） process is dominated by the transfer function of the difference operator, which is the same as the result of Crato. The obtained asymptotically properties of the single random walk process was extended to more complicated autoregressive integrated moving average process.展开更多
文摘青海省位于中国西部,拥有丰富的草地资源和独特的畜牧业生产条件,使得该地区的肉类产量在全国占有重要地位。肉类产量不仅直接影响当地牧民的收入水平,还关系到青海省的经济发展和社会稳定。有鉴于此,采用R studio软件建立自回归差分移动平均(ARIMA)模型可对青海省的肉类产量进行历史数据分析和未来趋势预测,并利用AIC准则确定模型的最优阶数。本文使用1997~2020年的青海省肉类产量作为数据源,在此基础上采用2021~2023年青海省肉类产量数据作为对比数据来判断真实值与预测值之间的差异,最终可得出其真实值与预测值之前有一定差异,但差异较小,整体预测精度较高。Qinghai Province, located in western China, has rich grassland resources and unique livestock production conditions, which make the meat production in the region occupy an important position in China. Meat production not only directly affects the income level of local herders, but also relates to the economic development and social stability of Qinghai Province. In view of this, the autoregressive integrated moving average (ARIMA) model can be built using R studio software to analyze the historical data and forecast the future trend of meat production in Qinghai Province, and the optimal order of the model can be determined using the AIC criterion. In this paper, the meat production of Qinghai Province from 1997 to 2020 is used as the data source, and on this basis, the meat production data of Qinghai Province from 2021 to 2023 is used as the comparative data to judge the difference between the real value and the forecast value, and finally, it can be concluded that there is a certain difference between the real value and the forecast value, but the difference is small, and the overall forecast accuracy is high.
文摘Based on the analysis of periodogram, this paper discussed the properties of periodogram of autoregressive integrated moving average process ARIMA（p,1 ,q）, and found that the periodgram of ARIMA（p,1 ,q） process is dominated by the transfer function of the difference operator, which is the same as the result of Crato. The obtained asymptotically properties of the single random walk process was extended to more complicated autoregressive integrated moving average process.
