COVID-19 Forecasting: Project Overview

In this project you will find Covid-19 cases prediction for the next 30 days, using Facebook Prophet model. More specifically:

• Visualization of daily COVID-19 cases (Globally & Greece)
• Visualization of daily COVID-19 death cases (Globally & Greece)
• Geographical visualization (Globally)
• COVID-19 Cases prediction for next 30 days (Globally & Greece)

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Resources used

Python Version: 3.7

Fbprophet Version: 0.6

Pystan Version: 2.19

Packages: numpy, pandas, matplotlib, plotly, sklearn

Fbprophet article: https://bit.ly/39gfmei

Fbprophet tutorial: https://bit.ly/3rpj3EF

Paper for Fbprophet: https://bit.ly/3lPh4Iy

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Time-Series Forecasting

The purpose of time series analysis is generally twofold: to understand or model the stochastic mechanisms that gives rise to an observed series and to predict or forecast the future values of a series based on the history of that series

Time-series forecast itself is one of the methods to create a model for predicting future values based on current and historical time series data. There are four components of time-series:

1. Trend: an increase or decrease of data; could be linear or nonlinear (logistics growth)
2. Seasonality: a characteristic of time-series data when it experiences regular and predictable movement after a fixed period of time
3. Cyclic: a cyclic pattern exists when the data experience rises or falls (regular or periodic fluctuation in data)
4. Irregularity: the residual of time series after the trend-cycle and seasonal component are removed

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Facebook Prophet

It’s a tool intended to help you to do time series forecasting at a scale with ease. It uses decomposable time series model with 3 main components: seasonal, trends, holidays or events effect and error which are combined into this equation:

f(x) = g(x) + s(x) + h(x) + e(t)

where,

g(x) is a trend function which models the non-periodic changes. It can be either a linear function or a logistic function.

s(x) represents a periodic changes i.e weekly, monthly, yearly. A yearly seasonal component is modeled using Fourier series and weekly seasonal component using dummy variables.

h(x) is a function that represents the effect of holidays which occur on irregular schedules.(n ≥ 1 days)

e(x) represents error changes that are not accommodated by the model.

Visualizations

Figure 1.1: Daily COVID-19 cases (Globally)

Figure 1.2: Daily COVID-19 cases (Greece)

Figure 1.3: Daily COVID-19 prediction cases (Greece)

Prophet plots the observed values of time series (black dots), the forecasted values (blue lines) and the uncertainty intervals of our forecasts (blue shaded region).