** The dsp**.MovingAverage System object™ computes the moving average of the input signal along each channel, independently over time. The object uses either the sliding window method or the exponential weighting method to compute the moving average. In the sliding window method, a window of specified length is moved over the data, sample by sample, and the average is computed over the data in the window. In the exponential weighting method, the object multiplies the data samples with a set of. result=movingmean(data,window,dim,option) computes a centered moving average of the data matrix data using a window size specified in window in dim dimension, using the algorithm specified in option. Dim and option are optional inputs and will default to 1. Dim and option optional inputs can be skipped altogether or can be replace with a []. For example movingmean(data,window) will give the same results as movingmean(data,window,1,1) or movingmean(data,window,[],1)

- movingAverage = conv (yourSignal, ones (101,1)/101, 'same'); For a 2D array of columns: movingAverage = conv2 (yourSignal, ones (101,1)/101, 'same'); If you don't want the central pixel to be included in the average and have ONLY the 50 on either side, use. kernel = ones (101,1)/100
- which returns. 1.5000 2.0000 3.0000 3.5000. The filter works as follows: 1 2 (1+2)/2 = 1.5 when k points at 1. 1 2 3 (1+2+3)/3 = 2.0 when k points at 2. 2 3 4 (2+3+4)/3 = 3.0 when k points at 3. 3 4 (3+4)/2 = 3.5 when k points at 4. Now it is easy to convert it to a logical code or merely use movmean ()
- The modified moving average is similar to the simple moving average. Consider the argument numperiod to be the lag of the simple moving average. The first modified moving average is calculated like a simple moving average. Subsequent values are calculated by adding the new price and subtracting the last average from the resulting sum
- g the summation in reverse (starting with the highest index x(n-0) and ending with the lowest index x(n-M-1)

Matlab's smooth function gives various ways of smoothing your data. The default is a moving average with a span of 5, but you can play around with this span or indeed try different smoothing techniques. For example: smooth (y) % Moving average with a window of 5 smooth (y, 3) % Moving average with a window of 3 smooth (y, 'sgolay', 3) % Savitz. ** MOVING will compute moving averages of order n (best taken as odd) Usage: y=moving (x,n [,fun]) where x is the input vector (or matrix) to be smoothed**. m is number of points to average over (best odd, but even works) y is output vector of same length as x

Because symmetric moving averages have an odd number of terms, a reasonable choice for the weights is b j = 1 / 4 q for j = ±q, and b j = 1 / 2 q otherwise. Implement a moving average by convolving a time series with a vector of weights using conv. You cannot apply a symmetric moving average to the q observations at the beginning and end of the series. This results in observation loss. One option is to use an asymmetric moving average at the ends of the series to preserve all observations. View MATLAB Command. Compute the three-point centered moving variance of a row vector and normalize each variance by the number of elements in the window. A = [4 8 6 -1 -2 -3 -1 3 4 5]; M = movvar (A,3,1) M = 1×10 4.0000 2.6667 14.8889 12.6667 0.6667 0.6667 6.2222 4.6667 0.6667 0.2500

- Hi everyone im kinda new with filter design in Matlab and in need of some help.. So basically i need to reduce the noise in an record and playback system based on DSP TMS320c6713. Right now im stuck in writing the code for Moving average filter (exponential or simple). so can somebody help me out or give me some examples please.
- Smooth the three signals using a moving average, and plot the smoothed data. x = 1:100; s1 = cos(2*pi*0.03*x+2*pi*rand) + 0.5*randn(1,100); s2 = cos(2*pi*0.04*x+2*pi*rand) + 0.4*randn(1,100) + 5; s3 = cos(2*pi*0.05*x+2*pi*rand) + 0.3*randn(1,100) - 5; A = [s1; s2; s3]; B = smoothdata(A,2); plot(x,B(1,:),x,B(2,:),x,B(3,:)
- Using a moving average to visualize time series dataThis video supports the textbook Practical Time Series Forecasting. http://www.forecastingbook.comhttp://..
- View MATLAB Command. Compute the three-point centered moving maximum of a row vector. When there are fewer than three elements in the window at the endpoints, take the maximum over the elements that are available. A = [4 8 6 -1 -2 -3 -1 3 4 5]; M = movmax (A,3) M = 1×10 8 8 8 6 -1 -1 3 4 5 5

In the MATLAB Home tab, create a new System object class by selecting New > System Object > Basic. The basic template for a System object opens in the MATLAB editor to guide you as you create the movingAverageFilter System object. Rename the class movingAverageFilter and save the file as movingAverageFilter.m * In Matlab to calculate a moving average movmean statement is used*. A moving average is commonly using along with time-series input data and the parameters of the moving average will be set according to application. If input arguments are a vector, then movmean operates along the length of the vector. If the input argument is a multidimensional array, then movmean operates along the first. Calculate the Simple Moving Average. Use the movavg function to calculate the simple moving average. Set the lag as 6, which indicates the window size or number of periods for the moving average. The window size of 6 represents 30 minutes of data. The default behavior for movavg is unweighted, or a simple moving average Hi, You got a new video on ML. Please watch: TensorFlow 2.0 Tutorial for Beginners 10 - Breast Cancer Detection Using CNN in Python https://www.youtube.com.. I have written a simple code that performs a 3-point moving average smoothing algorithm. It is meant to follow the same basic algorithm as Matlab's smooth(...) function as described here. However, the result of my code is very different from that of Matlab's. Matlab's 3-point filter appears to perform a much more aggressive smoothing

Code:clcclear allclose allt=0:0.11:20;x=sin(t);n=randn(1,length(t));x=x+n;a=input('Enter the no.:');t2=ones(1,a);num=(1/a)*t2;den=[1];y=filter(num,den,x);plo.. in this video you will get the understanding of the code about **moving** **average** filter clear allclcn=0:100s1=cos(2*pi*0.05*n)%low frequency sinosoids2=cos(2*pi.. Calculate the Moving Average for a Data Series. View MATLAB Command. Load the file SimulatedStock.mat, which provides a timetable ( TMW) for financial data. load SimulatedStock.mat type = 'linear' ; windowSize = 14; ma = movavg (TMW_CLOSE,type,windowSize) ma = 1000×1 100.2500 100.3433 100.8700 100.4916 99.9937 99.3603 98.8769 98.6364 98.4348. M = movmean(___,dim) returns the array of moving averages along dimension dim for any of the previous syntaxes. For example, if A is a matrix, then movmean(A,k,2) operates along the columns of A, computing the k-element sliding mean for each row. example. M = movmean(___,nanflag) specifies whether to include or omit NaN values from the calculation for any of the previous syntaxes. movmean(A,k. Exponential moving average is a weighted moving average, where timeperiod is the time period of the exponential moving average. Exponential moving averages reduce the lag by applying more weight to recent prices. For example, a 10 period exponential moving average weights the most recent price by 18.18%

Can anyone help me to compute three point moving average of a 5 year data.I used the filter command but the result are erroneous .I am using MATLAB 2015.And I have a huge data 5 year day wise data and i have to compute three point moving average for each month A moving average filter smooths data by replacing each data point with the average of the neighboring data points defined within the span. This process is equivalent to lowpass filtering with the response of the smoothing given by the difference equation . y s (i) = 1 2 N + 1 (y (i + N) + y (i + N − 1) +... + y (i − N)) where y s (i) is the smoothed value for the ith data point, N is the.

Description. The Moving Average block computes the moving average of the input signal along each channel independently over time. The block uses either the sliding window method or the exponential weighting method to compute the moving average. In the sliding window method, a window of specified length moves over the data sample by sample, and the block computes the average over the data in. You can choose any weights b j that sum to one. To estimate a slow-moving trend, typically q = 2 is a good choice for quarterly data (a 5-term moving average), or q = 6 for monthly data (a 13-term moving average). Because symmetric moving averages have an odd number of terms, a reasonable choice for the weights is b j = 1 / 4 q for j = ±q, and b j = 1 / 2 q otherwise 3 which a moving average might be computed, but the most obvious is to take a simple average of the most recent m values, for some integer m. This is the so-called simple moving average model (SMA), and its equation for predicting the value of Y at time t+1 based on data up to time t is The moving average model of order q is deﬁned to be Z t = + a t + 1a t 1 + 2a t 2 + + qa t q where 1; 2;::: q are parameters in R. The above model can be compactly written as Z t = + (B)a t where (B) is the moving average operator. Deﬁnition (Moving Average Operator) The moving average operator is (B) = 1 + 1B+ 2B2 + + qBq Arthur Berg Yule-Walker Equations and Moving Average Models 7/ 9. MovingAverage 可翻译为滑动 exp moving average的matlab代码 09-09. exp moving average的matlab 代码. tf.train.ExponentialMovingAverage()函数解析（最清晰的解释） 种树最好的时间是10年前，其次是现在!!! 03-29 5650 近来看batch normalization的代码时，遇到tf.train.ExponentialMovingAverage()函数，特此记录。 TensorFlow官网上对于.

Exponential Moving Average. The Exponential Moving Average filter (EMA) is a very useful filter for smoothing all kinds of data, and it can be implemented very easily and efficiently. On top of that, it is a great way to enrich your understanding of digital filters in general in this video you will get the understanding of the code about moving average filter clear allclcn=0:100s1=cos(2*pi*0.05*n)%low frequency sinosoids2=cos(2*pi.. * Moving Average Model MA(q) ModelThe moving average (MA) model captures serial autocorrelation in a time series y t by expressing the conditional mean of y t as a function of past innovations, ε t − 1, ε t − 2, , ε t − q*.An MA model that depends on q past innovations is called an MA model of degree q, denoted by MA(q).. The form of the MA(q) model in Econometrics Toolbox™ i If you want a moving average, just maintain N samples in a ring buffer. Together with the current total. When you add a new sample, you adjust the total by subtracting the previous entry and adding the new entry to the total. You choose the algorithm that is most suitable for your requirements

- g linear correlation analysis, data fitting, and Fourier analysis. Typically, the first step to any data analysis is to plot the data.
- Description. The dsp.MovingAverage System object™ computes the moving average of the input signal along each channel, independently over time. The object uses either the sliding window method or the exponential weighting method to compute the moving average. In the sliding window method, a window of specified length is moved over the data, sample by sample, and the average is computed over.
- In MATLAB ®, the filter Moving-Average Filter of Traffic Data. Open Live Script. The filter function is one way to implement a moving-average filter, which is a common data smoothing technique. The following difference equation describes a filter that averages time-dependent data with respect to the current hour and the three previous hours of data. y (n) = 1 4 x (n) + 1 4 x (n-1) + 1 4 x.
- Moving averages are often used in time series analysis, for example in ARIMA models and, generally speaking, when we want to compare a time series value to the average value in the past. How are the moving averages used in stock trading? Moving averages are often used to detect a trend. It's very common to assume that if the stock price is above its moving average, it will likely continue.
- Moving (Running) Average using LabVIEW function without loop. Today I learned one trick from NI discussion forum to calculate the Moving (Running) Average without using loop. FIR filter LabVIEW function used for this calculation. See the attached screenshot & VI snippet code below. at November 25, 2015

Matlab rechnet nach dem IEEE Standard für Fließkommazahlen. Die Eingabe >> b = 1/1111101 * 1/3 liefert die Ausgabe 10. 2 Grundlagen b = 3.000027300248433e-07 Dies ist gleichbedeutend mit dem Wert 3.000027300248433 · 10−7. Die Endung e^gibt immer den Wert der Zehnerpotenzen an. Wechseln wir wieder mittels format shortzur Standard-Ausgabe in Matlab , so würde der Befehl >> b = 1/1111101. The moving average (MA) filter is perhaps one of the most widely used FIR filters due to its conceptual simplicity and ease of implementation. As seen in the diagram below, notice that the filter doesn't require any multiplications, just additions and a delay line, making it very suitable for many extreme low-power embedded devices with basic computational capabilities. However, despite its. Details. The moving average is a running average computed over a moving window over the length of the EMG.Usually, the EMG signal is first rectified due that, generally, the mean value of an EMG signal is zero.. The window length is the double of the value of wsize in samples. The units of the window size could be in number of samples (samples) or in seconds (time)

- Moving average smoothing is a naive and effective technique in time series forecasting. It can be used for data preparation, feature engineering, and even directly for making predictions. In this tutorial, you will discover how to use moving average smoothing for time series forecasting with Python. After completing this tutorial, you will know: How moving average smoothing works and some.
- RSI of Moving Average - page 33. 222675. Mladen Rakic 2014.04.10 07:13. 2014.04.10 16:13:25. #321. fajst_k: Yes, RSIOMA using both iMA and iMAOnArray and after replacing RSI implementation with Wilders. one (so like in MATLAB) MT4 chart still does not match MATLAB chart, its only 'more or less'
- g is the 4001 length one. Otherwise the effect of the glitch is still present. The only thing I can see wrong now is that the envelope doesn't match the.

** Finding the moving averages will help you identify the trend as you will see in the next 2 examples**. Example 1. The temperatures measured in London for the first week in July were as follows: 21⁰C, 24⁰C, 21⁰C, 27⁰C, 30⁰C, 28.5⁰C and 36⁰C. Calculate all of the 3 point moving averages and describe the trend. 1 st 3 point moving average Moving average. Learn more about moving average . Toggle Main Navigatio Multiple-pass moving average filters involve passing the input signal through a moving average filter two or more times. The spectrum can then be plotted using any of MATLAB's three-dimensional plotting routines such as mesh or surf. The next example provides an example of the use of these routines to determine the spatial frequency characteristics of the moving average filter. Example 8.

* The moving average filter is a special case of the regular FIR filter*. Both filters have finite impulse responses. The moving average filter uses a sequence of scaled 1s as coefficients, while the FIR filter coefficients are designed based on the filter specifications. They are not usually a sequence of 1s The output of a smoothing, linear spatial filter is simply the average of the pixels contained in the neighborhood of the filter mask. These filters sometimes are called averaging filters. For reasons explained in they also are referred to a low pass filters. The idea behind smoothing filters is straightforward. By replacing the value of every pixel in an image by the average of the gray.

Data over which the block computes the **moving** **average**. The block accepts real-valued or complex-valued multichannel inputs, that is, m -by- n size inputs, where m ≥ 1 and n ≥ 1. The block also accepts variable-size inputs. During simulation, you can change the size of each input channel. However, the number of channels cannot change In de statistiek is een voortschrijdend gemiddelde, wel afgekort aangeduid met MA (Engels: moving average) het gemiddelde van een vast aantal opeenvolgende elementen in een tijdreeks.Bepaalde periodieke verschijnselen in een tijdreeks kunnen door een geschikte keuze van de periode uitgemiddeld worden, zodat het voortschrijdend gemiddelde het verloop op de langere termijn toont

The Hull Moving Average is a powerful trend-following overlay indicator that can be used to determine trends and capture them with less lag than the simple moving average. Its calculation is mor We previously introduced how to create moving averages using python. This tutorial will be a continuation of this topic. A moving average in the context of statistics, also called a rolling/running average, is a type of finite impulse response. In our previous tutorial we have plotted the values of the arrays x and y: Let' M = mean (A,'all') 计算 A 的所有元素的均值。. 此语法适用于 MATLAB ® R2018b 及更高版本。. M = mean (A,dim) 返回维度 dim 上的均值。. 例如，如果 A 为矩阵，则 mean (A,2) 是包含每一行均值的列向量。. M = mean (A,vecdim) 计算向量 vecdim 所指定的维度上的均值。. 例如，如果 A 是. Tutorial with MATLAB Michalis Vlachos IBM T.J . Watson Research Center Hawthorne, NY, 10532 Tutorial | Time-Series with Matlab 2 About this tutorial The goal of this tutorial is to show you that time-series research (or research in general) can be made fun, when it involves visualizing ideas, that can be achieved with concise programming. Matlab enables us to do that. Will I be able to use.

Moving Average ブロックは、時間の経過に沿って入力信号の移動平均を各チャネルで個別に計算します。このブロックでは、スライディング ウィンドウ法または指数の重み付け法のどちらかを使用して移動平均を計算します。スライディング ウィンドウ法では、データ サンプル上で指定の長さの. Calculating the Simple Moving Average in your Google Sheets document is useful as it makes your spreadsheet dynamic and flexible over time. It becomes increasingly easy to get lost in the rows and columns of data if you're not careful with hard coding formulas and ranges. That's why in the case of the Simple Moving Average, it's best to use a changing formula while you use your.

Exponential Moving Average (EMA): Unlike SMA and CMA, exponential moving average gives more weight to the recent prices and as a result of which, it can be a better model or better capture the movement of the trend in a faster way. EMA's reaction is directly proportional to the pattern of the data. Since EMAs give a higher weight on recent data than on older data, they are more responsive to. The moving average is computed based on a moving time window. The moving average for continuous-time is calculated as. where: u (t) is the input signal, f is the fundamental frequency of the signal. The moving average for discrete-time is calculated as * Mein MATLAB Forum : Gast > Registrieren Auto? HOME; Forum ich hab nur leider erhebliche probleme moving average anzuwenden*. kann mir da jemand behilflich sein? im anhang nochmal ein beispiel plot zur verdeutlichung. Neu Bitmap.JPG Beschreibung: Download Dateiname: Neu Bitmap.JPG Dateigröße: 55.49 KB Heruntergeladen: 426 mal: Titus: Forum-Meister Beiträge: 871: Anmeldedatum: 19.07. Moving Average (Feedforward) Filters I. Simple digital ﬁlters Suppose that we have a sequence of data points that we think should be characterizable as a smooth curve, for example, increasing in value and then decreasing. Suppose further that the data roughly follow the expected form, but there is some irregularity in the curve that we assume is some kind of noise. (1) The MATLAB vector X.

Moving Average using a for loop. Learn more about moving average How plot **MOVING** **AVERAGE**?. Learn more about **moving** **average** ma yahoo finance exce

- Moving Average Matlab Glatt. Ein einfacher (ad hoc) Weg ist, nur einen gewichteten Durchschnitt (abstimmbar durch alpha) an jedem Punkt mit seinen Nachbarn: oder einige Variation davon. Ja, um anspruchsvoller zu sein, können Sie Fourier transformieren Sie Ihre Daten zuerst, dann schneiden Sie die hohen Frequenzen. So etwas wie: Dies schneidet die höchsten 20 Frequenzen. Achten Sie darauf.
- Published: 29 July 2014; Rainfall data analyzing using moving average (MA) model and wavelet multi-resolution intelligent model for noise evaluation to improve the forecasting accuracy. Seyed Ahmad Akrami 1, Ahmed El-Shafie 1, Mahdi Naseri 2 & Celso A. G. Santos 3 Neural Computing and Applications volume 25, pages 1853-1861 (2014)Cite this.
- The difference equation of an exponential moving average filter is very simple: y [ n] = α x [ n] + ( 1 − α) y [ n − 1] In this equation, y [ n] is the current output, y [ n − 1] is the previous output, and x [ n] is the current input; α is a number between 0 and 1. If α = 1, the output is just equal to the input, and no filtering.

* Sine Wave Code*. Audio Code. We can use MATLAB to visualize the effects of the filter. The scripts used can be found at the bottom of the page. First, we generate a test signal that consists of two sine waves. Then we apply the filter to it and plot the result. You can clearly see how the high-frequency sine wave is attenuated Moving Average Matlab Conv. 29 September, 2013 Gleitender Durchschnitt durch Faltung Was ist gleitend Durchschnitt und was ist es gut für Wie ist die gleitende Mittelung durch Faltung durchgeführt Moving Average ist eine einfache Operation, die gewöhnlich verwendet wird, um Rauschen eines Signals zu unterdrücken: Wir setzen den Wert jedes Punktes auf die Durchschnitt der Werte in seiner. Rafael Varago. Sep 4, 2016 · 6 min read. Hello, Today, I'm going to talk about a simple and commonly used linear filter known as moving average filter. We'll discuss the importance and usage of this filter, some aspects of its description and along the text, I'll give a implementation of a moving average filter in MATLAB for smoothing a.

Return the moving average of 2D matrix X, given a running window The difference equation of the Simple Moving Average filter is derived from the mathematical definition of the average of N values: the sum of the values divided by the number of values. y [ n] = 1 N ∑ i = 0 N − 1 x [ n − i] In this equation, y [ n] is the current output, x [ n] is the current input, x [ n − 1] is the previous input, etc Help Moving Average Matlab. Mit MATLAB, wie kann ich den 3-tägigen gleitenden Durchschnitt einer bestimmten Spalte einer Matrix finden und den gleitenden Durchschnitt an diese Matrix anschließen Ich versuche, den 3-tägigen gleitenden Durchschnitt von unten nach oben der Matrix zu berechnen, die ich mir zur Verfügung gestellt habe Code. Geben Sie die folgende Matrix a und mask. Ich habe. Moving Average Filter. Some time series are decomposable into various trend components. To estimate a trend component without making parametric assumptions, you can consider using a filter. Filters are functions that turn one time series into another. By appropriate filter selection, certain patterns in the original time series can be clarified.

Backtesting simple moving average trading strategy. Learn more about trading Financial Instruments Toolbo We don't know what your data are. Months have different numbers of days in them. If you have missing days or missing months, you should first run interp1 on it to fill them in. Conv() has options 'same', 'valid', and 'full' depending on how you want to handle the edge effects where your window reaches the beginning and end of your data where you don't have the full 6 months of data moving average in eeglab. Learn more about moving average

MA(Moving Average)移动平均线指标是将一定时间段的市场价格进行移动平均值，求出一个趋势值，用来作为价格走势的研判工具，它的目的是由平均数值来形成一个趋势图如图所示：MA指标具体的计算如下：MA指标属性里有四个不同的移动平均线选择，如下图：所以相对应的有四种不同的计算方式：SMA. ** Autoregressive-moving-average model with exogenous inputs model (ARMAX model) The notation ARMAX ( p, q, b) refers to the model with p autoregressive terms, q moving average terms and b exogenous inputs terms**. This model contains the AR ( p) and MA ( q) models and a linear combination of the last b terms of a known and external time series The exponential moving average (EMA) is a weighted average of recent period's prices. It uses an exponentially decreasing weight from each previous price/period. In other words, the formula gives recent prices more weight than past prices. For example, a four-period EMA has prices of 1.5554, 1.5555, 1.5558, and 1.5560 Use Git or checkout with SVN using the web URL. Work fast with our official CLI. Learn more . If nothing happens, download GitHub Desktop and try again. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. Your codespace will open once ready. There was a problem preparing your codespace. exponential moving average + matlab I got some pretty good results. /PB. Matthew Crema 2005-03-15 17:22:22 UTC. Permalink. Post by John Meares Hello Would anyone have a script that computes an exponential moving average? Thanks John. I'm not sure what you mean by an exponential moving average. In general you can compute a moving average by sliding a window function along the waveform. The.

Conditioned moving average window. Learn more about moving average, window siz Hi, Thanks for all your great indicators. Would it be possible for you to update the FRAMA to use the new ETF HQ modified version that they say is a lot better. http.

Moving Average. Learn more about moving average, average, moving windo There are a number of ways to calculate a moving average in T-SQL, but in this tip we will look at a way to calculate a moving average that sets the averaging window x number of rows behind and x number of rows ahead of the current data row. The advantage of this is that there is no lag in the average value returned and the moving average value is on the same row with its current value. Let's. Simple moving averages (SMAs) are calculated by the sum of data points in a time interval divided by the number of time periods therein. For example, a standard 10-day moving average on a.

Compared to the Simple Moving Average, the Linearly Weighted Moving Average (or simply Weighted Moving Average, WMA), gives more weight to the most recent price and gradually less as we look back in time. On a 10-day weighted average, the price of the 10th day would be multiplied by 10, that of the 9th day by 9, the 8th day by 8 and so on. The total will then be divided by the sum of the. Is there a Unit Root Test for Moving Average... Learn more about unit root, test, moving average ** The moving-average filter is more or less perfect for smoothing data in the presence of noise, if the useful information in your data is completely in the time domain**.In that case, you don't care about its rather poor performance in the frequency domain.Figure 1 shows the impulse, step, and frequency responses of the basic moving-average filter (with three extra samples on both sides that.

Excel cannot calculate the moving average for the first 5 data points because there are not enough previous data points. 9. Repeat steps 2 to 8 for interval = 2 and interval = 4. Conclusion: The larger the interval, the more the peaks and valleys are smoothed out. The smaller the interval, the closer the moving averages are to the actual data points. 7/10 Completed! Learn more about the. Erstellt am Mittwoch, den 08. Oktober 2008 um 20 04 Letzte Aktualisierung am Donnerstag, den 14. März 2013 01 29 Geschrieben von Batuhan Os.. Moving average is a type of arithmetic average. The only difference here is that it uses only closing numbers, whether it is stock prices or balances of account etc. The first step is to gather the data of the closing numbers and then divide that number by for the period in question, which could be from day 1 to day 30 etc. There is also another calculation, which is an exponential moving.