Assessing the informational content of card transactions for nowcasting retail trade: Evidence for Latvia

We use a novel card transaction data maintained at the Central Bank of Latvia to assess their informational content for nowcasting retail trade in Latvia. During the COVID-19 pandemic in Latvia, the retail trade turnover dynamics underwent drastic changes reflecting the various virus containment measures introduced during three separate waves of the pandemic. We show that the nowcasting model augmented with card transaction data successfully captures the turbulence in retail trade turnover induced by the COVID-19 pandemic. The model with card transaction data outperforms all benchmark models in the out-of-sample nowcasting exercise and yields a notable improvement in forecasting metrics. We conduct our nowcasting exercise in forecast-as-you-go manner or in real-time squared;that is, we use real-time data vintages, and we make our nowcasts in real time as soon as card transaction data become available for the target month. © 2023 The Authors. Journal of Forecasting published by John Wiley & Sons Ltd.

Modeling and Predicting the COVID-19 Trajectory in India

The SARS-CoV-2 has a confirmed case count of about 11.3 million and a death count of about 158,000 in India as of March 13th, 2021. Despite the early social distancing and lockdown measures imposed by the government, these counts have continued to rise. Mathematical models prove extremely useful to predict the course of the pandemic and for the government to strategize accordingly. Over due course several models have emerged to predict the number of COVID-19 cases, but a thorough comparison among them is lacking. In this paper, we propose three novel Hybrid Models based on the compartment-based modeling over data from January 22nd, 2020 to December 3rd 2020 and then make comparisons among them and show through experiments that each performs a better fitting and prediction on the Johns Hopkins COVID-19 dataset pertaining to India than all other benchmark models discussed. Comparison of our proposed Hybrid models with the existing compartment models like SIR, SIRD and SEIRD demonstrates that our proposed Hybrid models not only overcome the performance inefficiencies related to the existing compartmental models but also achieve a better fitting on the Johns Hopkins COVID-19 dataset. © 2022 IEEE.

An Ensemble Approach for Short-Term Load Forecasting for DISCOMS of Delhi Across the COVID-19 Scenario

The Covid-19 has presented unforeseen challenges to the world that has never been experienced before in history. None of the sectors remained unaffected witnessed various changes in their day-To-day operations. The impact has also been observed in the power sector, which can easily be illustrated with load fluctuations. The balancing of load supply in the energy sector is itself one of the critical complex tasks which becomes more vulnerable to deviation in case of these unforeseen events. Despite using advanced systems like machine learning artificial intelligence for load forecasting, utilities found the task challenging. This paper covers the impact of lockdown on load patterns of the Discoms of Delhi in the year 2020-21. The effect of weather on load is also analysed to demonstrate the critical correlation between them. The performance of the ensemble technique that has been proven beneficial for better load forecasting has outperformed other existing models, even in the current pandemic situation, has also been analysed validated through a comparative analysis against popular benchmark models. © 2021 IEEE.