This repository provides a set of sample cases for TimeEngine Python SDK.
- Import CSV and write:
$ ./forecast/arima.py -w -t 1 --read_back create test scenario: 1 OK: Data are validated
- Read and validate against CSV:
$ ./forecast/arima.py -v -t 1 OK: Data are validated
- Print info about User/Swimlane:
$ ./forecast/arima.py -i -t 1 { "adm_secret": "...", "app": "...", "adm": "...", "app_secret": "..." }
- Modeling:
$ ./forecast/arima.py -t 1 --model_country="Germany" --model_p=5 --model_q=3 --model_n=20 OK: Data are read
--model_country="Germany" see labels --model_p see 'p' param in model descrition --model_q see 'q' param in model descrition --model_n forecast number
- Clean data:
$ ./forecast/arima.py -d -t 1 clean {u'adm_secret': u'...', u'app': u'...', u'adm': u'...', u'app_secret': u'...'}
Data sources:
covid_time_series.csv is a fragment of the following file: https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv
earthquake*.csv files: All data collected from this page should be used only for scientific and non-commerical purposes. Upon use of our data, proper attribution should be given to B.U. KOERI-RETMC (Boğaziçi University Kandilli Observatory and Earthquake Research Institute - Regional Earthquake-Tsunami Monitoring Center) in scientific articles and general purpose reports by referencing the KOERI Catalog citation.
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