Time Series Forecasting

the below input parameters are for different attack types to start working with the apis view the docid\ eb7fsmb2o fz1 rlafm9u file upload format data data should be in a csv file with a header as all the features (columns) name and the last column as the target variable https //aisdocs blob core windows net/reference/upload/timeseries/forecasting/ml/data zip minmax data should be in a csv file with a header as all the feature (columns) names and the last column as the target variable the first row of the csv file should contain the minimum value for each column (feature), and the second row should contain the max value https //aisdocs blob core windows net/reference/upload/timeseries/forecasting/ml/minmax zip model the model should be saved in either pkl, h5 or tensorflow format with full architecture full architecture is needed when loading the model to the platofrm for assessment either in encrypted or unencrypted this can be ignored when model is hosted as an api https //aisdocs blob core windows net/reference/upload/timeseries/forecasting/ml/model0 zip all files uploaded should be in zipped format the above files are sample data extraction parameters parameter data type descrption remark model id string model id received during model registration we need to provide this model id in query parameter in url you have to do model registration only once for a model and you can perform many analysis this will help you to track how many api call has been made, how many has successed request body (json format) attack type string you can select the attack type either blackbox or greybox blackbox for performing model analysis, no information about model or data will be used greybox information about data will be leverage for creation of attack data note only 2 5 % of data is needed input dimensions string input dimension (example 100,6 for pump sensor) the parameter should be string number of attack queries string number of attack queries that model will be subjected to generally heigher the number of attack queries, better would be the analysis and it would take more time to process (range >0 & <=400000) model framework string original model is built with tensorflow framework (option \[tensorflow]) curretly supported framework are tensorflow, scikit learn, keras model api details string if use model api is yes, then provide api details of hosted model as encrypted json string is mandatory provide this only if use model api is "yes" use model api string use model api to train your model instead of uploading the model as a zip when this parameter is yes, you don't have to upload model as zip you can pass api url along with other verification credential in json file defense bestonly string highly optimized defense model will be returned when selected "yes" , it will train n number of model and select best model ofcourse this will take longer time if "no" , then once defense model accuracy reached above 95% it will stop encryption strategy int choose a encryption strategy for you model if model is uploaded directly as a zip pick 0, 1 if model is encryted as pyc and uploaded as a zip ignore if use model api is yes select 0 pass tensorflow model as it is, select 1 pass encrypted model it could be pyc file vulnerability threshold string threshold percent of stolen model accuracy at which defense model should be generated parameter value will be between 0 to 1 to access all sample artifacts, please visit docid\ ijneocxostabvvrsq11fa for specific artifact details, refer vulnerability report docid\ hl0ut2mwlcbkt8f97fr w sample attacks docid 4g1mjm5lqjfm8t5wbvwpr defense report docid\ vtzlttpja2vsf2j0stlsq defense model docid\ xsbxmzxw4vv14 8nmbf8m note for time series forecasting, supported attack types are extraction