Azure Machine Learning - forecasting time series based on different ARIMA models

I'm looking for someone who can help me with setting up a simple azure machine learning project for forecasting time series based on ARIMA models The routine should enable to 1) Drag and Drop time series (in Excel format) 2) enable to manually set-up to two different ARIMA models. The manual set-up of the two should concern (1) the quantity of data that the ARIMA model is processing (the section of the time series that is used to do the predictions), (2) the time lag between the data that is used for training the ARIMA model) and the prediction 3) a third ARIMA model, which is doing forecasts based on the mean of “ARIMA model 1” and “ARIMA model 2”. It should be possible to manually weight the influence of “ARIMA model 1” and “ARIMA model 2” for the third ARIMA model 4) compare the accuracy of the three different ARIMA based on mean absolute percentage error (MAPE) The routine should be built based on this description: [login to view URL] (Build and deploy forecasting models with Azure Machine Learning) The person should: - Setup the azure machine learning project with the goals as described above - be available for possible follow-up projects Requirements: - Examples of early project you have done on Azure Machine Learning - Examples / proof of other projects on forecasting time series (with ARIMA) - High feedback/recommendations Note: the project should start with this simple proof of concept. After this a simulation project is required. In the following project it should be possible to simulate the accuracy of the three models if different features of the time series (volatility) and the relationship of the ARIMA models 1 and 2 (difference in information processing quantity, difference in time lag) change. The results of the simulations should be visualized Looking forward to the biddings. Thanks and best regards Moritz