Microsoft Excel Double Exponential Smoothing Example FormulaPatent US6. Method for determining optimal time series forecasting parameters. Instruction Manual For Tomtom Start 25 Argos on this page. CROSS REFERENCE TO RELATED APPLICATIONSThis application claims priority from U. S. Provisional Patent Application No. Sep. 1. 7, 1. 99. The entire disclosure of the provisional application is considered to be part of the disclosure of the accompanying application and is hereby incorporated by reference. A method for forecasting a value of a dependent variable, such as product demand, in a future time period later than the next, upcoming future time period. The method. FIELD OF THE INVENTIONThe present invention relates to a new method for calculating time series forecasting parameters that includes looking forward to improve forecasting accuracy for supply chain needs beyond the next time period. In particular, the invention is directed to a method for estimating the parameters of a selected forecasting method such that the forecast is not necessarily most accurate in the next, upcoming time period but is instead optimized to give a more accurate forecast for some specific, user selected future time period. The method is also applicable to estimate time series forecasting parameters when it is necessary to optimize a time series forecast for two or more future time intervals combined. BACKGROUND OF THE INVENTIONOrganizations need to carefully plan and be prepared for their future business in order to be successful, and the required planning includes understanding supply chain complexities and predicting future behaviors and values of planning variables, such as product demand. For example, organizations must plan to have sufficient product available to meet demand while not having excess product that may quickly become stale or out dated. Outdated inventory is a serious and potentially costly problem for companies that manufacture and sell high technology and other products with shorter life spans. Games Written in Fortran See Also GraphicsGUI Development Tools in Compatible Products CrazyFortran An example of programming fun i. NOT to do it. Exponential smoothing is a rule of thumb technique for smoothing time series data. Whereas in the simple moving average the past observations are weighted equally. Smoothing algorithms are basically used in time series data either to produce smoothed data for presenting the trend of the data or to forecast it for what if. Origin and OriginPro Introduction. Origin is the data analysis and graphing software of choice for over half a million scientists and engineers in commercial. Nick Douglas. Staff Writer, Lifehacker Nick has been writing online for 11 years at sites like Urlesque, Gawker, the Daily Dot, and Slacktory. Tuning Software for MSII v2. The Windows 9xMEXPVista software application you use to tune and configure your MegaSquirt or MegaSquirtII is either Tuner. Power BI supports different methods for connecting data. That is why the decision to choose the right method is always a tough decision. You have heard of DirectQuery. One measure of organizational success used by organizations for planning purposes is revenue. Consequently, as a part of planning, organizations need to accurately estimate future income as well as carefully plan future expenses in order to calculate expected revenues. One method used to calculate expected revenues is to estimate future income from historical demand or sales information and then budget expenses from the estimate of future income. However, this method does not address the complexities of supply chains and the impact supplier characteristics can have on providing components that are later utilized to manufacture products to meet forecasted demand. Another method to calculate expected revenues is to estimate both future income and future expenses from historical information and then evaluate the reasonableness of the estimated expenses to determine if the forecasted expenses need adjusting. Both of these approaches allow an organization to determine a relationship between income, expenses, and revenues, and of course, there are numerous other methods that are utilized by organizations for strategic planning. Estimating income and other planning variables from past or historical information is always necessary regardless of which of the planning approaches is selected. Typically, some form of forecasting process uses the historical information to provide these estimations. As a specific example, organizations estimate future income by forecasting the future demand for each individual product. This approach is used since each product is likely to produce a different income per unit. ECz5G4Te8/maxresdefault.jpg' alt='Microsoft Excel Double Exponential Smoothing Example Excel' title='Microsoft Excel Double Exponential Smoothing Example Excel' />Microsoft Excel Double Exponential Smoothing Example YoutubeServoHydraulic Test Controller Review. Please note that this review is now archived, and will not be updated. The information was current in the fall of 2004. Use features like storage analytics, clientside logging, and other thirdparty tools to identify, diagnose, and troubleshoot Azure Storagerelated issues. In addition to providing information about estimated income for the planning group of the organization, the forecasted demand provides information that is relatively consistent across the organization for other groups within the organization to use. For example, if the organization manufactures a product, the forecasted demand for each product is often used to determine the effect on the organizations ability to manufacture sufficient products. The manufacturing group needs to determine if their manufacturing capabilities are adequate and if they have sufficient inventories of component parts to produce the potential product demand. Without adequate manufacturing capability or sufficient inventory, the organization may not be able to manufacture enough products to produce planned incomes. This would reduce income and adversely affect expected revenues. The manufacturing group also relies on forecasted demand to allocate its resources efficiently, balance workload against the forecasted demand, and plan their operations to meet the needs the product demand places on them. Without this manufacturing planning, manufacturing may not be able to produce products in a timely manner, which would limit the availability of the product for the customer when the customer wants to purchase the product. This would also decrease sales and reduce income and adversely affect expected revenues. Manufacturing organizations occasionally use just in time manufacturing. This manufacturing approach minimizes excess inventory by providing the component parts to assemble a product just in time for the assembly process. This reduces or even eliminates any component part inventory, which minimizes the risk of manufacturing components becoming outdated. However, even organizations that use the just in time approach must accurately determine how many and when each component will be needed. Such a determination again involves forecasting or predicting future demand andor need that will exist in the next time period and, more likely, in the next several time periods to allow components to be ordered and delivered by a supplier. A lead time problem often occurs when ordering component parts. Some suppliers can provide component parts with little or no notice, i. Other component part suppliers may require the manufacturer to place orders for components several months in advance, even as much as six months in advance. The reasons behind this requirement may range from the length of time needed to make the component part to transportation delays for shipping the parts from manufacturing sites abroad and to a simple need to be able to plan their own future manufacturing effort. The varying lead time problem is made even more significant if these long lead time components are also very expensive. For example, the manufacturer of some expensive components, such as computer components, may require orders be placed three months in advance and these orders may also be non cancelable. Consequently, accurately forecasting how many of these components a company will need in three months or a time period coinciding with a suppliers required lead time, is a very important part of effective organization planning, such as inventory management. Further in this regard, manufacturing organizations do not want to inaccurately order expensive components because having too few components will result in lost income from the product but having too many components will result in unnecessary expenses. Also, sometimes these expensive components have a short lifetime of usefulness and having excess inventory of outdated components may lead to wasted expense if these components need to be discarded. Clearly, accurate forecasting is important to the success of a business or other organization, but a perfectly accurate forecast is not possible, even with existing forecasting software products and computer implemented computation methods.