Скачать с ютуб Approaches to Forecasting II Moving average II Weighted moving average II Exponential smoothing в хорошем качестве

Approaches to Forecasting II Moving average II Weighted moving average II Exponential smoothing

Approaches to Forecasting There are two general approaches to forecasting: qualitative and quantitative Two general approaches Quantitative methods consist mainly of subjective inputs, which often defy precise numerical description. Quantitative methods involve the extension of historical data Quantitative techniques consist mainly of analyzing objective, or hard, data Qualitative techniques permit inclusion of soft information (e.g., human factors, personal opinions, hunches) in the forecasting process. Those factors are often omitted or downplayed when quantitative techniques are used because they are difficult or impossible to quantify Type of forecast FORECASTS BASED ON JUDGMENT AND OPINION . FORECASTS BASED ON TIME SERIES (HISTORICAL) DATA ASSOCIATIVE FORECASTS Forecasts Based on Judgment and Opinion Forecasts Based on Time Series Data Forecasting techniques based on time series data are made on the assumption that future values of the series can be estimated from past values. Although no attempt is made to identify variables that influence the series, these methods are widely used, often with quite satisfactory results Analysis of time series data requires the analyst to identify the underlying behavior of the series. This can often be accomplished by merely plotting the data and visually examining the plot. One or more patterns might appear: trends, seasonal variations, cycles, and variations around an average. In addition, there can be random or irregular variations. These behaviors can be described as follows: TECHNIQUES FOR AVERAGING Averaging techniques generate forecasts that reflect recent values of a time series (e.g., the average value over the last several periods). These techniques work best when a series tends to vary around an average, although they can also handle step changes or gradual changes in the level of the series. Three techniques for averaging are described in this section: Moving average 2. Weighted moving average 3. Exponential smoothing #ApproachesToForecasting #MovingAverage #WeightedMovingAverage #ExponentialSmoothing Moving average moving average Technique that averages a number of recent actual values, updated as new values become available. One weakness of the naive method is that the forecast just traces the actual data Weighted Moving Average A weighted average is similar to a moving average, except that it assigns more weight to the most recent values in a time series Exponential Smoothing Exponential smoothing is a sophisticated weighted averaging method that is still relatively easy to use and understand. Each new forecast is based on the previous forecast plus a percentage of the difference between that forecast and the actual value of the series at that point. Associative Forecasting Techniques Associative techniques rely on identification of related variables that can be used to predict values of the variable of interest. For example, sales of beef may be related to the price per pound charged for beef and the prices of substitutes such as chicken, pork, and lamb; real estate prices are usually related to property location; and crop yields are related to soil conditions and the amounts and timing of water and fertilizer applications. The essence of associative techniques is the development of an equation that summarizes the effects of predictor variables.