The repository also comes with Azure Machine Learning (Azure ML) themed notebooks and best practices recipes to accelerate the development of scalable, production-grade forecasting solutions on Azure. You will find the following examples for forecasting with Azure AutoML as well as tuning and deploying a forecasting model on Azure. Developing an accurate forecasting solution can be a complex and time-consuming process. We hope the forecasting repo will help shorten your development cycle on Azure.
Forecasting accuracy is a critical factor for, among other things, reducing costs and providing better customer service. Yet the knowledge and experience available for improving such accuracy for specific situations is not always utilized. The consequence is actual and/or opportunity losses, sometimes of considerable magnitude. Empirical studies in the field of forecasting have compared the post-sample forecasting accuracy of various methods so that their performance can be determined in an objective, measurable manner. The M-Competitions are such empirical studies that have compared the performance of a large number of major time series methods using recognized experts who provide the forecasts for their method of expertise.
Using the technique employed in the article, I would build a forecasting model for the expenditure, and do a forecast for each day until the end of the year, and would try to find the date at which you come closest to the $10,000 limit. Obviously longer the forecasting time period, more uncertain your estimation will be. I don't how heavy the calculations gets if you try to do a very long period forecasting, but if so, then you might have to do one forecasting at a time, and check every time if you are below or above the expenditure threshold of $10K. You would also need to consider the risk of over expenditure, whether you are ok with 1% chance or 5% etc.
A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. Apart from introduction and references the paper is organized as follows. The section 2 presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.
Demand management solutions have improved on many fronts. One area where some solutions have made particular progress is in the area of providing highly flexible solutions that can evolve as the company's business changes. Demand management solutions need to be flexible! Over time, companies often go on a journey where their forecast accuracy is improved by using more and more data and by forecasting at a more granular levels (For more on this topic, see this article which describes stages in demand forecasting maturity). To support this journey demand management solutions need to be flexible.