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THE ISSUE OF LAG Besides their ability to decrease the amount of noise in a time series, moving averages are versatile, easy to understand, and readily calculated However, as with any well-damped low-pass filter or real-time data smoothing procedure, reduced noise comes at a cost: lag Although smoothed data may contain less noise and, therefore, be easier to analyze, there will be a delay, or lag, before events in the original time series appear in the smoothed series Such delay can be a problem when a speedy response to events is essential, as is the case for traders Sometimes lag is not an issue, eg, when a moving average of one time series is predictive of another series This occurs when the predictor series leads the series to be predicted enough to compensate for the lag engendered by the moving average.



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It is then possible to benefit from noise reduction without the cost of delay Such a scenario occurs when analyzing solar phenomena and seasonal tendencies Also, lag may not be a serious problem in models that enter when prices cross a moving average line: In fact, the price must lead the moving average for such models to work Lag is more problematic with models that use the slope or turning points in the average to make trading decisions In such cases, lag means a delayed response, which, in turn, will probably lead to unprofitable trades A variety of adaptive moving averages and other sophisticated smoothing techniques have been developed in an effort to minimize lag without giving up much noise reduction One such technique is based on standard time series forecasting methods to improve moving averages To eliminate lag, Mulloy (1994) implements a linear, recursive scheme involving multiple moving averages.

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considered to be very close to toll-quality. This four-bit transcoding process, which is based on the known behavior characteristics of human voice, allows the system to transmit 8,000, four-bit samples per second, thus reducing the overall bandwidth requirement from 64 Kbps to 32 Kbps. It should be noted that ADPCM works well for voice, because the encoding and predictive algorithms are based upon its behavior characteristics. It does not, however, work as well for higher bit-rate data (above 4,800 bps), which has an entirely different set of behavior characteristics.

Continuously Variable Slope Delta (CVSD) is a unique form of voice encoding that relies on the values of individual bits to predict the behavior of the incoming signal. Instead of transmitting the volume (height or y-value) of PAM samples, CVSD transmits information that measures the changing slope of the waveform. So, instead of transmitting the actual change, it transmits the rate of change. To perform its task, CVSD uses a reference voltage, to which it compares all incoming values. If the incoming signal value is less than the reference voltage, then the CVSD encoder reduces the slope of the curve to make its approximation better mirror the slope of the actual signal. If the incoming value is more than the reference value, then the encoder will increase the slope of the output signal, again causing it to approach and, therefore, mirror the slope of the actual signal. With each recurring sample and comparison, the step function can be increased or decreased as required. For example, if the signal is increasing rapidly, then the steps are increased one after the other in a form of step function by the encoding algorithm. Obviously, the reproduced signal is not a particularly exact representation of the input signal: In practice, it is pretty jagged. Filters, therefore, are used to smooth the transitions. CVSD is typically implemented at 32 Kbps, although it can be implemented at rates as low as 9,600 bps. At 16 to 24 Kbps, recognizability is still possible; down to 9,600, recognizability is seriously affected, although intelligibility is not.

Mandatory Silverlight core runtime library (such as System.dll, System.core.dll)

When the rate of movement in the market is appropriate to the filter, lag is eliminated; however, the filters tend to overshoot (an example of insufficient damping) and deteriorate when market behavior deviates from filter design specifications Chande ( 1992) took a nonlinear approach, and developed a moving average that adapts to the market on the basis of volatility Sometimes lag can be controlled or.

Linear Predictive Coding (LPC)

We mention LPC here only because it has carved out a niche for itself in certain voice-related applications such as voice mail systems, automobiles, aviation, and electronic games that speak to children. LPC is a

eliminated by combining several moving averages to create a band-pass filter. Band-pass filters can have effectively zero lag for signals with periodicities near the center of the pass-band; the smoothed signal can be coincident with the original, noisy signal when there is cyclic activity and when the frequency (or periodicity) of the cyclic activity is close to the frequency maximally passed by the filter.

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