CSAR: the cross-sectional autoregression model for short and long-range forecasting
CSAR: the cross-sectional autoregression model for short and long-range forecasting
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DOI：
10.1007/s41060-018-00169-7
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发表时间：
2019-01
影响因子：
2.4
通讯作者：
Claudio Hartmann;F. Ressel;M. Hahmann;Dirk Habich;Wolfgang Lehner
中科院分区：
文献类型：
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作者：
Claudio Hartmann;F. Ressel;M. Hahmann;Dirk Habich;Wolfgang Lehner
时间序列数据的预测是管理、规划和决策不可或缺的组成部分。随着大数据趋势的发展，海量的时间序列数据在许多应用领域都可用。这些领域的高度动态和经常有噪音的特点，再加上从大量数据来源收集数据的逻辑问题，对预测过程提出了新的要求。为了能够以较高的精度和较短的执行时间进行长期规划，必须在几个时期内预测数量不断增加的时间序列。这几乎是不可能的，因为保持传统的重点是为每个单独的时间序列创建一个预测模型。此外，经常使用的预测技术，如ARIMA，需要完整的历史数据，如果时间序列是间歇性的，则会失败。满足所有这些新要求的一种方法是横断面预测法。它利用同一模型中同一领域的多个时间序列的可用数据，从而可以对缺失值进行补偿，从而快速计算出准确的预测结果。然而，这种方法受到数据选择僵化的限制，现有的预测方法表明，模型对数据的适应性提高了预测精度。因此，在本文中，我们提出了一种CSAR模型，它通过增加更多的灵活性来扩展横截面范式，并允许对分析数据进行细粒度的适应。通过这种方式，我们实现了更高的预测精度，从而获得了更广泛的适用性。
The forecasting of time series data is an integral component for management, planning, and decision making. Following the Big Data trend, large amounts of time series data are available in many application domains. The highly dynamic and often noisy character of these domains in combination with the logistic problems of collecting data from a large number of data sources imposes new requirements on the forecast process. A constantly increasing number of time series has to be forecast over several periods in order to enable long-term planning with high accuracy and short execution time. This is almost impossible, when keeping the traditional focus on creating one forecast model for each individual time series. In addition, often used forecast techniques like ARIMA require complete historical data and fail if time series are intermittent. A method that addresses all these new requirements is the cross-sectional forecasting approach. It utilizes available data from many time series of the same domain in one single model; thus, missing values can be compensated and accurate forecast results are calculated quickly. However, this approach is limited by a rigid data selection and existing forecast methods show that adaptability of the model to the data increases the forecast accuracy. Therefore in this paper, we present CSAR, a model that extends the cross-sectional paradigm by adding more flexibility and allows fine-grained adaptations toward the analyzed data. In this way, we achieve an increased forecast accuracy and thus a wider applicability.