These Models For My Garaduation Projecet. I have created these three models for forecasting the next 7 days new, recovery and death cases caused by covid-19 virus.
I have singled out Egypt by these Models. I have get the data-set using these APIs:
I have used FacebookProphet For Forcasting.
Facebook developed an open sourcing Prophet, a forecasting tool available in both Python and R. It provides intuitive parameters which are easy to tune. Even someone who lacks deep expertise in time-series forecasting models can use this to generate meaningful predictions for a variety of problems in business scenario.
“Producing high quality forecasts is not an easy problem for either machines or for most analysts. We have observed two main themes in the practice of creating a variety of business forecasts: • Completely automatic forecasting techniques can be brittle, and they are often too inflexible to incorporate useful assumptions or heuristics. • Analysts who can product high quality forecasts are quite rare because forecasting is a specialized data science skill requiring substantial experience.”
- Very fast, since it is built in Stan, a programming language for statistical inference written in C++.
- An additive regression model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects: 1. A piecewise linear or logistic growth curve trend. Prophet automatically detects changes in trends by selecting changepoints from the data 2. A yearly seasonal component modeled using Fourier series 3. A weekly seasonal component using dummy variables 4. A user�provided list of important holidays.
- Robust to missing data and shifts in the trend, and typically handles outliers.
- Easy procedure to tweak and adjust forecast while adding domain knowledge or business insights.
The Prophet uses a decomposable time series model with three main model components: trend, seasonality, and holidays. They are combined in the following equation:
y(t)= g(t) + s(t) + h(t) + εt
- g(t): piecewise linear or logistic growth curve for modeling non-periodic changes in time series
- s(t): periodic changes (e.g. weekly/yearly seasonality)
- h(t): effects of holidays (user provided) with irregular schedules
- εt: error term accounts for any unusual changes not accommodated by the model
- Using time as a regressor, Prophet is trying to fit several linear and nonlinear functions of time as components. Modeling seasonality as an additive component is the same approach taken by exponential smoothing in Holt-Winters technique . Facebook Prophet is framing the forecasting problem as a curve-fitting exercise rather than looking explicitly at the time-based dependence of each observation within a time series.
