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Technology forecasting using time series intervention based trend impact analysis for wheat yield scenario in India

机译：使用基于时间序列干预的趋势影响分析技术预测印度小麦的产量情景

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In conventional Trend Impact Analysis (TIA), a baseline model based forecast is generated using historical data. Also, a set of future events and their impacts are identified utilizing prior knowledge. Further, these impacts and events are combined with baseline to generate possible future scenarios through simulation. One of the main drawback of this approach is that it cannot deal with unprecedented future technologies or rare events. Further, it cannot answer about expected future, if some specific event occurs at a particular period in future. Intervention analysis has been traditionally used to assess the impact of any unprecedented event occurring at known times on any time series. It consists of a single impact parameter and a slope parameter for a particular event. Hence, a new TIA method has been developed by combining conventional TIA with the intervention model instead of simulation, The traditional interventional model were modified as per the requirement of TIA to incorporate three impact parameters for any number of events. For the unprecedented future event, impact of the event is known while time at which event will occur is not known in advance. A formula for estimating slope parameter has been derived. The proposed TIA approach is capable to handle the influence of any unusual occurrences on the structure of the fitted model while providing forecasts of future values. The data requirements in this proposed new TIA is less as compared to conventional TIA approach, It can also answer about expected future if some particular event occur in particular time. The proposed TIA approach has been empirically illustrated for wheat yield scenario at All-India level. For this, three events each with three degrees of severity have been considered. All possible scenarios were generated from which preferable futures can be chosen. (C) 2017 Elsevier Inc. All rights reserved.
机译：在常规趋势影响分析（TIA）中，使用历史数据生成基于基线模型的预测。而且，利用现有知识可以识别出一组未来事件及其影响。此外，将这些影响和事件与基准相结合，以通过模拟生成可能的未来方案。这种方法的主要缺点之一是它无法应对前所未有的未来技术或罕见事件。此外，如果某个特定事件在将来的特定时间段发生，它就无法回答预期的未来。传统上，干预分析通常用于评估在已知时间发生的任何史无前例的事件对任何时间序列的影响。它由单个冲击参数和特定事件的斜率参数组成。因此，通过将传统的TIA与干预模型相结合而不是模拟来开发了一种新的TIA方法。根据TIA的要求对传统的干预模型进行了修改，以针对任意数量的事件纳入三个影响参数。对于前所未有的未来事件，事件的影响是已知的，而事件发生的时间是事先未知的。推导了估算坡度参数的公式。提出的TIA方法能够处理任何异常事件对拟合模型结构的影响，同时提供对未来价值的预测。与传统的TIA方法相比，该提议的新TIA中的数据要求更少。如果某些特定事件在特定时间发生，它也可以回答预期的未来。针对全印度水平的小麦单产情景，对拟议的TIA方法进行了经验说明。为此，已经考虑了三个事件，每个事件的严重性为三个级别。生成了所有可能的方案，可以从中选择更合适的期货。 （C）2017 Elsevier Inc.保留所有权利。

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