Following the schema described in the training and prediction workflow document, this is the code snippet that shows the minimal workflow to create a time series and produce a forecast.
;; step 0: creating a source from the data in your remote "https://raw.githubusercontent.com/bigmlcom/python/master/data/grades.csv" file
(define source-id (create-source {"remote" "https://raw.githubusercontent.com/bigmlcom/python/master/data/grades.csv"}))
(log-info "Creating remote source: " source-id)
;; step 1: creating a dataset from the previously created `source`
(define dataset-id (create-dataset source-id))
(log-info "Creating dataset from source: " dataset-id)
;; step 2: creating a time series
(define timeseries-id (create-timeseries dataset-id))
(log-info "Creating time series from dataset: " timeseries-id)
;; the new input data to forecast
(define input-data {"000005" {"horizon" 10}})
;; creating the forecast
(define forecast-id (create-forecast timeseries-id
{"input_data" input-data}))
(log-info "Creating forecast for some input data: " forecast-id)
;; the forecast resource contains a property where it stores
;; the forecast value.
(define forecast-resource (fetch forecast-id))
;; extracting the forecast value from the resource
(define forecast (forecast-resource ["forecast" "result"]))
(log-info "The forecast for " input-data " is : " forecast)
You can test this code in the WhizzML REPL.
To learn more about the arguments that can be set in the forecast and
timeseries creation calls and the response properties, please have a look
at the API documentation.