Genius Tool to Compare Best Time-Series Models For Multi-step Time Series Modeling

The intensity of the growth of the covid-19 pandemic worldwide has propelled researchers to evaluate the best machine learning model that could the people affected in the distant future by considering the current statistics and predicting the near future terms in subsequent stages. While different univariate models like ARIMA/SARIMA and traditional time-series are capable of predicting Number of Active cases, daily recoveries, Number of deaths, they do not take into consideration the other time-varying factors like Medical Facilities (Volume of Testing, ICU beds, Hospital Admissions, Ventilators, Isolation Units, Quarantine Centres, etc). As these factors become important we build a predictive model that can predict the Number of Active Cases, Deaths, and Recoveries based on the change in Medical Facilities as well as other changes in infrastructure. Here in this blog, we try to model Multi-step Time Series Prediction using Deep learning Models on the basis of Medical Information available for different states of India. A typical multi-step predictive model looks as the below figure, where each of the predicted outcomes from the previous state is treated as next state input to derive the outcome for the second-state and so forth.