The current paper aims to analytically visualize the future outcomes that the post-pandemic India might have in store for its citizens. We use time series forecasting on various collected data and combined the statistics of economics-deciding parameters to forecast the trends that might be prevalent in the next year. Since, the data contains a single anomalous trend, even the Prophet model could not learn this property from the data since this trend is not seasonal in nature. The current study proposes a novel architecture to deal with these rare unusual trends by combining two models - one learning normal usual patterns and the other getting trained on usual as well as rare anomalous patterns. It could help in dealing with sudden hike patterns like due to COVID-19 in the data, and lead to better forecasting on future timeframes. We combined the results of two distinct time-forecasting models trained on two sets of data of varying timeline lengths, using parameters obtained from Least Squares Approximation (LSA). The LSA helps us find an approximate vector approximation so as to obtain a model performing closely to the actual.

中文翻译：

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通过时间序列预测和最小二乘近似预测大流行后印度失业率的影响


本文旨在分析、可视化大流行后的印度可能为其公民带来的未来结果。我们对收集到的各种数据进行时间序列预测，并结合经济决定参数的统计来预测明年可能流行的趋势。由于数据包含单一异常趋势，因此即使 Prophet 模型也无法从数据中了解此属性，因为此趋势本质上不是季节性的。当前的研究提出了一种新颖的架构，通过结合两种模型来处理这些罕见的异常趋势——一个学习正常的常见模式，另一个接受常见和罕见的异常模式的训练。它可以帮助处理突然的加息模式，例如由于数据中的 COVID-19，并可以更好地预测未来的时间范围。我们使用从最小二乘近似 (LSA) 获得的参数，结合了在不同时间线长度的两组数据上训练的两个不同时间预测模型的结果。 LSA帮助我们找到一个近似的向量近似值，从而获得一个与实际情况接近的模型。