From:http://www.cnblogs.com/kemaswill/archive/2013/04/01/2993583.htmlIn the time series, we need to predict the following trend based on the current data of the time series, and the three exponential smoothing (Triple/three Order exponential smoothing,holt-winters) algorithm can predict the time series well.Time series
of this period (t phase);? The actual value of the yt--t period;? The predicted value of the YT '--t period, that is, the smoothed value of the previous period St-1.Example: It is known that the sales of a product in the last 15 months are shown in the following table:Predict the next month's sales Y16 with a single exponential smoothing value.In order to analyze the characteristics of the different values
be described roughly as an additive model, so we can use the simple exponential smoothing method to predict. We use the Holtwinters () function in R, in order to be able to use the exponential smoothing in holtwinters, we need to set parameters: Beta=false and Gamma=false, and predict the results such as:Holtwinters (
My friends and I shared the simple exponential smoothing method, simple exponential smoothing can only predict those at a constant level and no seasonal changes in the time series, today and you share the non-constant level of growth or reduce the trend, The time series prediction algorithm without seasonal additive mo
In a time series, we need to predict its future trend based on the existing data of the time series. The Three exponent Smoothing (Triple/Three Order Exponential Smoothing, Holt-Winters) the algorithm can well predict the time series.
Time series data generally has the following characteristics: 1. Trend (Trend) 2. Seasonal (Seasonality ).
The trend describes the
The exponential smoothing, similarities, and differences mean line (macd) is developed based on the moving average line. It uses two different speeds (a short-term moving average line with a fast change rate, the exponential smoothing moving average line of a long moving average line with a slow change speed is used to
The previous article describes how to perform continuous updates in the query mode. This blog article describes how to implement exponential smoothing in streaminsight.Concepts
Before implementation, let's take a look at what the exponential smoothing method is?
Concepts: The expon
forecast is based on a balance between the most recent and long-term observations. Beta 0 indicates that the slope of the trend part is listed unchanged throughout the time series and is equal to the initial value, this also conforms to our intuitive feeling, the level changes very much, but the trend part slope is basically invariable, on the contrary gamma=0.96 indicates that the current season partial forecast
Exponential Smoothing MethodThe original number data is as follows:Click Data--Data analysisSelect exponential SmoothingBest-in- one smoothingSince the area we selected was b1:b22, the first cell "steel output" was used as a sign, so we should tick the mark. When we tick the flag, the first cell in the column is not used for the calculation, and the calculation s
Exponential Smoothing Similarities and Differences moving average[Macd]
■Exponential Smoothing Similarities and Differences moving average[Macd] -- it is an indicator constructed using two exponentially weighted moving averages. It can be us
Original address:Http://blog.csdn.net/qustmeng/article/details/52186378?locationNum=4fps=1Import java.util.LinkedList;Import java.util.List;public class Demo {/*** Two times exponential smoothing method for predicting values* @param list Base data collection* @param year of the next few installments* @param modulus Smoothing coefficient* @return Predictive value*
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