High School When will ball 25 drop? Predicting future observations using data

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The discussion focuses on predicting the drop time of billiard balls based on collected observational data. The user seeks to develop an algorithm to estimate when future balls will drop, emphasizing a preference for a step-by-step approach rather than complex mathematical formulas. Participants suggest that using the average drop time and considering linear regression could improve prediction accuracy, while also noting the importance of accounting for variations in data collection efficiency and external factors. The conversation also touches on the challenges of ensuring data integrity and the potential biases in observations. Overall, the goal is to refine the prediction model for better accuracy as more data becomes available.
  • #31
mfb said:
Sure. Your dataset has "plate 54: date, plate 55: date, plate 57: date, plate 58: date, ...
Right. Which is why you originally introduced a distinct index.

I was treating the array index as the x-axis.
i.e. my values would end up being:
item[23]: CDJ
item[24]: CDK
item[25]: CDM
So, CDL is not there, and therefore no gap where there should be a gap, putting the next item out of line.

For you:
item[23]: {index:23, plate: CDJ}
item[24]: {index:24, plate: CDK}
item[25]: {index:26, plate: CDM}
So the trend is conserved.

I see now.
 
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  • #32
I think what you may be looking for is Kalman Filter.
 

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