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项目作者： atecon

项目描述 ：
ARIMA-based forecasting of confirmed COVID/ Corona cases for various country-province combinations
高级语言： Shell
项目地址: git://github.com/atecon/covid_19_forecast.git
创建时间： 2020-02-28T11:43:13Z
项目社区：https://github.com/atecon/covid_19_forecast
开源协议：
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Time-series based 7-days ahead forecasts of (known) confirmed cases of the novel Corona virus COVID-19 (2019-nCoV)

Archived since 2021-01-12: This repo is archived but still can be forked.

Statement: This is just “hobby” project and forecast results should not be taken too seriously! Even though forecast performance may be reasonable for some country and some period in time. Don’t take the numbers forecast for granted!
This project is mainly supposed to show what may be realized (without too much effort) with the open-source statistics and econometrics software gretl (URL: http://gretl.sourceforge.net/).

Minimum gretl version: At least gretl 2020b.

An automated job retrives latest data at 3am (CET), trains a new model, computes the forecasts and uploads the new forecasting plots here to my github-repo. The overall job finishes in about 15 seconds on a Raspberry Pi 4 computer —- this is just an amazing piece of hardware ;-)

Data source

Data provided by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) can be found here:
https://github.com/CSSEGISandData/COVID-19

Some words on the underlying model

I want to keep it simple. If you fancy, you can use the current state of code as a starting point for devloping and evaluating more complex approaches. However, the ARIMA type of model applied may already provide a reasonable approach for modelling and computing short-term contagion dynamics.

I recently (2020-07-26) switched from simple pre-defined ARIMA models to my official auto_arima package for gretl automatcally searching for the “best” model. Details on the auto_arima package can be found here:
https://github.com/atecon/auto_arima

The code executes a brute-force search for the ‘best’ ARIMA model specification by optimizing the corrected Akaike information criteria (“aicc”). The following parameter space — implying 120 different ARIMA models in total — is evaluated:

Default ARIMA parameter space:

  1. string ARIMA_OPTS.INFO_CRIT = "aicc"        # information criteria to optimize
  2. scalar ARIMA_OPTS.min_p = 0                    # autoregressive (AR) order
  3. scalar ARIMA_OPTS.max_p = 4                 # autoregressive (AR) order
  4. scalar ARIMA_OPTS.min_d = 0                 # differencing order
  5. scalar ARIMA_OPTS.max_d = 2                 # differencing order
  6. scalar ARIMA_OPTS.min_q = 0                 # moving average (MA) order
  7. scalar ARIMA_OPTS.max_q = 1                 # moving average (MA) order
  9. scalar ARIMA_OPTS.min_P = 0                 # seasonal autoregressive (AR) order
  10. scalar ARIMA_OPTS.max_P = 1                 # seasonal autoregressive (AR) order
  11. scalar ARIMA_OPTS.min_D = 0                 # seasonal differencing order
  12. scalar ARIMA_OPTS.max_D = 0                 # seasonal differencing order
  13. scalar ARIMA_OPTS.min_Q = 0                 # seasonal MA order
  14. scalar ARIMA_OPTS.max_Q = 1                 # seasonal MA order

Forecasting method

We compute out-of-sample multi-period interval forecasts. The multi-period forecast is recursively computed. Per default the 90 % forecast (Gaussian) interval will be shown as well.

The gretl script

The gretl script for setting up relevant things and executing the analysis is ./script/run.inp.

At the beginning of the script, the user can specify the following parameters:

  1. string DIR_WORK = ""         # <SET_PATH_HERE> e.g. "/home/git_project"
  2. string DATA_URL = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv"    # Data source
  3. string INITIAL_DATE = "2020-01-22"            # 1st available observation of CSSE dataset
  4. scalar MINIMUM_CASES = 30                    # Minimal number of confirmed cases at latest observation for consideration
  5. string SAVE_PLOT_AS = "png"                    # grpahic format: "png", "pdf"", eps"
  7. # ARIMA model settings -- see auto_arima package: http://ricardo.ecn.wfu.edu/gretl/cgi-bin/current_fnfiles/auto_arima.gfn
  8. bundle ARIMA_OPTS = null
  9. scalar ARIMA_OPTS.MAX_HORIZON = 7           # max. multi-step OoS forecast horizon
  10. # optimize by information criteria, either aic, aicc, bic or hqc
  11. string ARIMA_OPTS.INFO_CRIT = "aicc"
  12. scalar ARIMA_OPTS.min_p = 0                    # autoregressive (AR) order
  13. scalar ARIMA_OPTS.max_p = 4                 # autoregressive (AR) order
  14. scalar ARIMA_OPTS.min_d = 0                 # differencing order
  15. scalar ARIMA_OPTS.max_d = 2                 # differencing order
  16. scalar ARIMA_OPTS.min_q = 0                 # moving average (MA) order
  17. scalar ARIMA_OPTS.max_q = 1                 # moving average (MA) order
  19. scalar ARIMA_OPTS.min_P = 0                 # seasonal autoregressive (AR) order
  20. scalar ARIMA_OPTS.max_P = 1                 # seasonal autoregressive (AR) order
  21. scalar ARIMA_OPTS.min_D = 0                 # seasonal differencing order
  22. scalar ARIMA_OPTS.max_D = 0                 # seasonal differencing order
  23. scalar ARIMA_OPTS.min_Q = 0                 # seasonal MA order
  24. scalar ARIMA_OPTS.max_Q = 1                 # seasonal MA order

This script will also load the functions doing the main stuff in the beckground which are stored in ./src/helper.inp as well as the 3rd party library auto_arima.

The gretl script can be executed in the following ways:
Option A:

  1. 1) Clone the repo by means of ```git clone```
  2. 2) open the script "./script/run.inp"
  3. 3) Set your project path by setting the variable "DIR_WORK" accordingly.
  4. 4) Execute and enjoy.

Option B (works for linux):

  1. 1) Clone the repo by means of ```git clone```
  2. 2) Execute the shell-script run.sh

What does the script do?

The script downloads latest available CCSE-data, processes the raw data for obtaining a clean panel data set.
Next, for each country-province combination two exercises are conducted:

  1. 1) If the parameter ```RUN_EXPOST_ANALYSIS``` is set to '1' , an **ex-post** forecasting analysis is done. For this,  the training-set is set to <CURRENT_DATE - MAX_HORIZON> observations where "CURRENT_DATE" refers to latest date for which data is available, and ```MAX_HORIZON``` is the set multi-step forecast horizon (default 7 days).
  2. 2) Out-of-sample interval forecasts for the forthcoming ```MAX_HORIZON``` days are computed.

Ex-Post and up-to-date out-of-sample 7-days ahead forecasts

The left panel shows forecast made in information available 7 days ago and the realization of ‘confirmed cases’ during this period. This may give you an idea of how ‘well’ the forecasted dynamics were.

The middle panel shows forecasts made on latest data for the forthcoming 7 days. The left panel depicts both the historic and predicted day-to-day changes of confirmed cases.


afghanistan -


albania -


algeria -


andorra -


angola -


antigua_and_barbuda -


argentina -


armenia -


australia - australian_capital_territory


australia - new_south_wales


australia - northern_territory


australia - queensland


australia - south_australia


australia - tasmania


australia - victoria


australia - western_australia


austria -


azerbaijan -


bahamas -


bahrain -


bangladesh -


barbados -


belarus -


belgium -


belize -


benin -


bhutan -


bolivia -


bosnia_and_herzegovina -


botswana -


brazil -


brunei -


bulgaria -


burkina_faso -


burma -


burundi -


cabo_verde -


cambodia -


cameroon -


canada - alberta


canada - british_columbia


canada - manitoba


canada - new_brunswick


canada - newfoundland_and_labrador


canada - nova_scotia


canada - ontario


canada - prince_edward_island


canada - quebec


canada - saskatchewan


central_african_republic -


chad -


chile -


china - anhui


china - beijing


china - chongqing


china - fujian


china - gansu


china - guangdong


china - guangxi


china - guizhou


china - hainan


china - hebei


china - heilongjiang


china - henan


china - hong_kong


china - hubei


china - hunan


china - inner_mongolia


china - jiangsu


china - jiangxi


china - jilin


china - liaoning


china - macau


china - ningxia


china - shaanxi


china - shandong


china - shanghai


china - shanxi


china - sichuan


china - tianjin


china - xinjiang


china - yunnan


china - zhejiang


colombia -


comoros -


congo_(brazzaville) -


congo_(kinshasa) -


costa_rica -


cote_d’ivoire -


croatia -


cuba -


cyprus -


czechia -


denmark - faroe_islands


denmark -


diamond_princess -


djibouti -


dominica -


dominican_republic -


ecuador -


egypt -


el_salvador -


equatorial_guinea -


eritrea -


estonia -


eswatini -


ethiopia -


fiji -


finland -


france - french_guiana


france - french_polynesia


france - guadeloupe


france - martinique


france - mayotte


france - reunion


france - saint_barthelemy


france - st_martin


france -


gabon -


gambia -


georgia -


germany -


ghana -


greece -


grenada -


guatemala -


guinea -


guinea-bissau -


guyana -


haiti -


honduras -


hungary -


iceland -


india -


indonesia -


iran -


iraq -


ireland -


israel -


italy -


jamaica -


japan -


jordan -


kazakhstan -


kenya -


korea_south -


kosovo -


kuwait -


kyrgyzstan -


latvia -


lebanon -


lesotho -


liberia -


libya -


liechtenstein -


lithuania -


luxembourg -


madagascar -


malawi -


malaysia -


maldives -


mali -


malta -


mauritania -


mauritius -


mexico -


moldova -


monaco -


mongolia -


montenegro -


morocco -


mozambique -


namibia -


nepal -


netherlands - aruba


netherlands - bonaire_sint_eustatius_and_saba


netherlands - curacao


netherlands - sint_maarten


netherlands -


new_zealand -


nicaragua -


niger -


nigeria -


north_macedonia -


norway -


oman -


pakistan -


panama -


papua_new_guinea -


paraguay -


peru -


philippines -


poland -


portugal -


qatar -


romania -


russia -


rwanda -


saint_lucia -


saint_vincent_and_the_grenadines -


san_marino -


sao_tome_and_principe -


saudi_arabia -


senegal -


serbia -


seychelles -


sierra_leone -


singapore -


slovakia -


slovenia -


somalia -


south_africa -


south_sudan -


spain -


sri_lanka -


sudan -


suriname -


sweden -


switzerland -


syria -


taiwan* -


tajikistan -


tanzania -


thailand -


timor-leste -


togo -


trinidad_and_tobago -


tunisia -


turkey -


us -


uganda -


ukraine -


united_arab_emirates -


united_kingdom - bermuda


united_kingdom - british_virgin_islands


united_kingdom - cayman_islands


united_kingdom - channel_islands


united_kingdom - gibraltar


united_kingdom - isle_of_man


united_kingdom - turks_and_caicos_islands


united_kingdom -


uruguay -


uzbekistan -


venezuela -


vietnam -


west_bank_and_gaza -


yemen -


zambia -


zimbabwe -