Ethereum. Predictive Modeling Pt 2. Model A Theory
מעודכן
This is Part Two of applying my theoretical geometric linear regression modeling to Ethereum. Part One, looked at finding Model A. We have found Model A, and its rendering is now complete. We are following the designated correction wave for entry into Model A. Were we enter into Model A, will allow me to figure out the starting trajectory for Model B.
Sequential Modeling begins with a foundation Model. In this case Model A is our foundation Model. All Models that are created after Model A, sequentially build off of one another. If a Model fails in the sequence, there are chain reaction like consequences that are unpredictable for the entire modeling sequence.
Model A works by having a lower and upper boundary. These boundaries are guided by higher order algorithms in the data. I look for any order algorithm to find stable lower and upper boundaries. Once i find stable boundaries, I look for statistical outliers (i.e. emotion and market manipulation). Outliers are data that significantly fall outside those stable boundaries. All outliers must re-enter the Model the left in order to remain an outlier dedicated to that Model. Each Model has a predicted Statistical Outlier. Confidence in each rendered Model resides in subjective understanding of the current state of the market both behaviorally and logistically.
In the grounding theory: "Belief (usually denoted Bel) measures the strength of the evidence in favor of a proposition p. It ranges from 0 (indicating no evidence) to 1 (denoting certainty). Plausibility is 1 minus the sum of the masses of all sets whose intersection with the hypothesis is empty. Or, it can be obtained as the sum of the masses of all sets whose intersection with the hypothesis is not empty. It is an upper bound on the possibility that the hypothesis could be true, i.e. it “could possibly be the true state of the system” up to that value, because there is only so much evidence that contradicts that hypothesis. Plausibility (denoted by Pl) is defined to be Pl(p) = 1 − Bel(~p). It also ranges from 0 to 1 and measures the extent to which evidence in favor of ~p leaves room for belief in p.
For example, suppose we have a belief of 0.5 and a plausibility of 0.8 for a proposition, say “the trend will go up”. This means that we have evidence that allows us to state strongly that the proposition is true with a confidence of 0.5. However, the evidence contrary to that hypothesis (i.e. “we will go down”) only has a confidence of 0.2. The remaining mass of 0.3 (the gap between the 0.5 supporting evidence on the one hand, and the 0.2 contrary evidence on the other) is “indeterminate,” meaning that we can go either up or down (+/- statistical outliers). This interval represents the level of uncertainty based on the evidence in the modeling sequence.
The plausibility of my Bel= 0.5 (Model A) and my proposition/prediction, 'the trend will go up' is P= 0.8. So i am saying Model A, will have a trend that goes up with a confidence of 80%, 20% being dedicated to the 'unknown'. The evidence of Model A is dictated by our foundation lines off of other algorithm orders, which are STRONG indicator s of truth, so we assign a level of confidence to my prediction. I now have subjective probability to support my belief, as long as i stay within my modeled rules and account for statistical outliers (uncertainty). I can now continue model sequencing, based on gathered subjected evidence to plausibly say my belief in Model (X) will result in prediction A or B with X uncertainty (+/- out). I then try to predict the location of uncertainty by using a best guess on where the Statistical outliers will occur, using geometric indicators. Anomalies can change this.. agree?
Evidence:
We have been in a downtrend for about 4 months.
We have hit close to bottom.
Lots of built up FOMO.
World news about crypto.
Big money is interested in low prices.
Hope is returning.
As always, thanks for looking!
Glitch420
Sequential Modeling begins with a foundation Model. In this case Model A is our foundation Model. All Models that are created after Model A, sequentially build off of one another. If a Model fails in the sequence, there are chain reaction like consequences that are unpredictable for the entire modeling sequence.
Model A works by having a lower and upper boundary. These boundaries are guided by higher order algorithms in the data. I look for any order algorithm to find stable lower and upper boundaries. Once i find stable boundaries, I look for statistical outliers (i.e. emotion and market manipulation). Outliers are data that significantly fall outside those stable boundaries. All outliers must re-enter the Model the left in order to remain an outlier dedicated to that Model. Each Model has a predicted Statistical Outlier. Confidence in each rendered Model resides in subjective understanding of the current state of the market both behaviorally and logistically.
In the grounding theory: "Belief (usually denoted Bel) measures the strength of the evidence in favor of a proposition p. It ranges from 0 (indicating no evidence) to 1 (denoting certainty). Plausibility is 1 minus the sum of the masses of all sets whose intersection with the hypothesis is empty. Or, it can be obtained as the sum of the masses of all sets whose intersection with the hypothesis is not empty. It is an upper bound on the possibility that the hypothesis could be true, i.e. it “could possibly be the true state of the system” up to that value, because there is only so much evidence that contradicts that hypothesis. Plausibility (denoted by Pl) is defined to be Pl(p) = 1 − Bel(~p). It also ranges from 0 to 1 and measures the extent to which evidence in favor of ~p leaves room for belief in p.
For example, suppose we have a belief of 0.5 and a plausibility of 0.8 for a proposition, say “the trend will go up”. This means that we have evidence that allows us to state strongly that the proposition is true with a confidence of 0.5. However, the evidence contrary to that hypothesis (i.e. “we will go down”) only has a confidence of 0.2. The remaining mass of 0.3 (the gap between the 0.5 supporting evidence on the one hand, and the 0.2 contrary evidence on the other) is “indeterminate,” meaning that we can go either up or down (+/- statistical outliers). This interval represents the level of uncertainty based on the evidence in the modeling sequence.
The plausibility of my Bel= 0.5 (Model A) and my proposition/prediction, 'the trend will go up' is P= 0.8. So i am saying Model A, will have a trend that goes up with a confidence of 80%, 20% being dedicated to the 'unknown'. The evidence of Model A is dictated by our foundation lines off of other algorithm orders, which are STRONG indicator s of truth, so we assign a level of confidence to my prediction. I now have subjective probability to support my belief, as long as i stay within my modeled rules and account for statistical outliers (uncertainty). I can now continue model sequencing, based on gathered subjected evidence to plausibly say my belief in Model (X) will result in prediction A or B with X uncertainty (+/- out). I then try to predict the location of uncertainty by using a best guess on where the Statistical outliers will occur, using geometric indicators. Anomalies can change this.. agree?
Evidence:
We have been in a downtrend for about 4 months.
We have hit close to bottom.
Lots of built up FOMO.
World news about crypto.
Big money is interested in low prices.
Hope is returning.
As always, thanks for looking!
Glitch420
הערה
Model A entry is imminent.. i think we make a head on collision with the tip of Model A. But of course i could be wrong.. הערה
we did it... Model A.. is true..הערה
It literally followed my arrows EXACTLY. Mind blown.. הערה
Correction wave is complete. Perfect prediction intersect. :)הערה
ok.. we ready for this? Target will be 950 by middle of May. Just a guess... ;)הערה
As you can see we have nicely dropped, which is within the modeled prediction zone. I have a feeling we may have our first statistical outlier somewhere along the lower boundary of Model A. Every model in my, 'Bitcoin to C.' chart, has a statistical outlier. I would expect no different here. Either we get a lower or upper boundary outlier per model. And of course, we can work thru the model as intended.. הערה
drop indicated. First statistical outlier like i said would happen..?הערה
if the HS pattern is right.. we just hit the peak of the RS. Down maybe?הערה
looks good to me.. but what do i know lol... הערה
peanut butter and jelly..הערה
הערה
It is just cruising along.. :)הערה
As you can see the evidence is building that statistical outlier #1 will manifest soon. הערה
We still gliding like butter on a tile floor.. הערה
WE pierced the lower boundary of Model A. Not far from where i had my my SO outlier indicator. הערה
We just chillen on the lower boundary.. According t my bitcoin chart, we are going to fall something nasty.. Which means Eth will fall, and we will have created our first statistical outlier.הערה
The inevitable will occurr.. goodnight.
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