Comparative Study Of Best Time-Series Models For Pandemic Response

Comparative Study Of Best Time-Series Models For Pandemic Response

With the effect of the pandemic increasing every day and casting a vehemently toxic influence in almost all parts of the world, it becomes important how can we contain the spread of the disease. In an effort to combat the disease every country has increased not only their testing facility but also the amount of medical help and emergency and quarantine centers.

With the effect of the pandemic increasing every day and casting a vehemently toxic influence in almost all parts of the world, it becomes important how can we contain the spread of the disease. In an effort to combat the disease every country has increased not only their testing facility but also the amount of medical help and emergency and quarantine centers. Here in this blog, we try to model Single-Step Time Series Prediction, using Deep Learning Models, on the basis of Medical Information available for different states of India.

Motivation:

Predict Number of Active Cases by Covid-19 Pandemic based on Medical Facilities (Volume of Testing, ICU beds, Ventilators, Isolation Units, etc) using _**_Multi-variate LSTM based forecasting**

Considering all these factors, it becomes important to have 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.

Single Step Time Series Prediction

One step time series prediction is a supervised machine learning task that comes with the functionality where the previous n-values are available when the next value in the time-series is predicted. In contrast, multi-step prediction involves prediction for x future steps.

The following figure depicts the different life cycle stages of time-series model training and prediction.

Source

  1. Feeding Multi-variate data from a single source or from aggregated sources available directly from the cloud or other 3rd-party providers into the ML modeling data ingestion system.
  2. Cleaning, preprocessing, and feature engineering of the data involving scaling **and normalization**.

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