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Found 3 results

1. ## A signal for training duration parameter in ForecastLinear

Hi all, So I am trying to create a linear forecast that has a constantly changing training duration as one of the parameter. For example : \$inputsignal.ForecastLinear(\$durationsignal,forecastduration) This is because sometimes the input signal has a step change and I would want to reset the forecast duration to start again from this step change. Is this something possible to execute? or is there any alternative method? Thanks all.
2. ## Replace Gaps in Data with an Average Value from Previous Time Frame

FAQ: I have a signal with a gap in the data from a system outage. I want to replace the gap with a constant value, ideally the average of the time period immediately before the data. Solution: 1. Once you've identified your data gaps, extend the capsules backwards by the amount over which time you want to take the average. In this example, we want to fill in the gap with the average of the 10 minutes before the signal dropped, so we will extend the start of the data gap capsule 10 minutes in the past. This is done using the move function in Formula: \$conditionForDataGaps.move(-10min,0min) 2. Use Signal from Condition to calculate the average of the gappy signal during the condition created in step 1. Make sure to select "Duration" for the timestamp of the statistic. 3. Stitch the two signals together using the splice function. The validvalues() function at the end ensures a continuous output signal. \$gappysignal.splice(\$replacementsignal,\$gaps).validvalues()
3. ## Comparing start and end values during an event

FAQ: I want to identify the change in value of my signal after a change has occurred. My signal is generally constant (with typical noise) aside from when an event occurs during which there is a step change in the value of the signal. Solution: In order to compare the actual value of the signal before and after some event, you must first identify the event. Method 1: Running Delta. Note: an approach using the running delta function can be preferred to the derivative function (Method 2) when step changes are present as the derivative is infinite during these steps. Using the derivative function works best if the signal is first cleansed/filtered and the interpolation method changed to linear. Use Seeq Formula to create a new signal that is the running delta of the original signal. Use the syntax: \$OrigianlSignal.runningdelta() Use the “Search Documentation” bar on the right side of the Formula window to learn more about the running delta function and syntax Once the new running delta signal has been created, do a value search on when it exceeds some threshold to identify the time periods over which you would like to know the start and end values. Note: if the step changes are occurring in both the increasing and decreasing directions, you can still identify all events by using Seeq Formula to take the absolute value of your running delta function (\$runningDeltaSignal.abs()) and applying the value search tool to the absolute value signal. The time periods identified using value search can be further manipulated to yield exactly the time periods that you are interested in using Seeq formula and the grow(), shrink(), and/or move() functions. Once the events during which the step change occurs have been identified, you can calculate statistics on them using the Signal from Condition tool. The inputs to the tool will be your original signal and the new condition that you’ve created to identify the events. A few statistics that you may be interested in: Delta: the end value minus the start value of the signal. This would give you the value drop (or gain) during one of these events. Value at start: this will grab the value of the original signal at the start of the event Value at end: this will grab the value of the original signal at the end of the event Method 2: Derivative. Best approach for most rate of change problems, excluding step changes. Use Low Pass Filter tool or Seeq Formula agileFilter() function to cleanse the original signal to remove typical noise from signal. Use Seeq Formula to create a new signal that is the derivative of the cleansed signal. Use the syntax: \$CleansedSignal.derivative() Use the “Search Documentation” bar on the right side of the Formula window to learn more about the derivative() function and syntax Once the new derivative signal has been created, do a value search on when it exceeds some threshold to identify the time periods over which you would like to know the start and end values. Note: if the value changes are occurring in both the increasing and decreasing directions, you can still identify all events by using Seeq Formula to take the absolute value of your derivative function (\$derivativeSignal.abs()) and applying the value search tool to the absolute value signal. The time periods identified using value search can be further manipulated to yield exactly the time periods that you are interested in using Seeq formula and the grow(), shrink(), and/or move() functions. Once the events during which the value change occurs have been identified, you can calculate statistics on them using the Signal from Condition tool. The inputs to the tool will be your original signal and the new condition that you’ve created to identify the events. A few statistics that you may be interested in: Delta: the end value minus the start value of the signal. This would give you the value drop (or gain) during one of these events. Value at start: this will grab the value of the original signal at the start of the event Value at end: this will grab the value of the original signal at the end of the event
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