Tag Archives: Predictions

Skysraper index offers recession predictions 

Skysraper index offers recession predictions

This came from the Telegram private group but I never knew about this

http://www.businessinsider.com/skyscraper-index-skyscrapers-signal-financial-recession-2012-1#the-long-depression-1873-1878-1

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

I no longer make these trading market predictions because…

I no longer make these trading market predictions because…

Dec 21, 2015
EURUSD is going up 
I am working on this new system to work on long term market direction at a macro economic level. Data is crucial and it never lies like humans. The progress is going quite amazing. It will virtually eliminate all human based guessed and emotion. I got creamed this year because of it so I hold until this thing gets built. Do realize there will always be trading opportunity in the markets so I don’t care about these predictions right now. I just hope to get this built really fast!

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

How is the market on Monday, any predictions?

How is the market on Monday, any predictions?

I got this from my contact form.

This looks like a spammy link but the question is funny

how is the market on monday, any predictions http://www.sgxniftystock.com

Join my FREE newsletter to learn more about making market predictions 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

How could we do predictions with data mining for quant analytics?

How could we do predictions with data mining for quant analytics?

 

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Predictive models can be grouped in two major groups: classification and regression models.
Both aim at building models that predicts the value of a variable knowing the values of other variables. Both models accept in input a set of training data. Each training instance has several attributes, one of which is the variable to be predicted. In classification, this variable is categorical and it is called class variable, in regression the variable is real–valued and is known as dependent variable. etc. Predictive models learn, using the
training data at hand, a mapping from the input variables to the dependent variable. The resulting model is then used to predict the value of the dependent variable for a new instance of which all the independent variables are known.
Examples of classification algorithms are: decision trees, neural network, nearest neighbor, rule-learners, etc..

 

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these videos may be helpful:http://www.youtube.com/user/11AntsAnalyticsTV and there is also a free trial here:http://www.11antsanalytics.com/

 

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Have you considered using JMP Pro, a tool from SAS that allows users to create predictive models visually?
http://www.jmp.com/uk/software/jmp9/pro/

 

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I have used PLS regression with XLSTAT

 

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I am using a hybrid PLS/NN method for regression. PLS is used for variables pruning and NN for prediction.

 

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Please, could you explain to me hybrid PLS/NN?

 

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This particuraly interesting when you have many variables. Prediction using NN with too many variables may be time complex and time consuming. So, you first perform a PLS to select most important variables (VIP with coeffeicient higher than 0.8 for example) and then do your NN prediction with reduced variables.

 

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Predictive model, in general are ‘Classification’ and ‘Regression’. The goal is to build a model where the value of one variable can be predicted from the values of other variable.

Classification is used for ‘categorical’ variables (i.e. Y/N, or answers for a variable like 1–5 for “like best” to “like least”).

Regression is used for “continuous” variables (e.g., variables where the values can
be any number, with decimals, between one number and another; age of a person would be an example, or blood pressure, or number of cases of a product coming off
an assembly line each day).

 

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Thanks for the informations, is it possible to apply PLS regression to predict for example binary materials in materials science? If yes, how could we do it?

 

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You should take a look at Krishna Rajan’s work for materials science predictions. For example:

The application of Principal Component Analysis to materials science data, Data Science Journal, Vol. 1 (2002) pp.19-26;

Materials informatics, Materials Today, Volume 8, Issue 10, October 2005, Pages 38–45

and quite a few others

 

 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

How could we do predictions with data mining for quant analytics?

How could we do predictions with data mining for quant analytics?

 

==

Predictive models can be grouped in two major groups: classification and regression models.
Both aim at building models that predicts the value of a variable knowing the values of other variables. Both models accept in input a set of training data. Each training instance has several attributes, one of which is the variable to be predicted. In classification, this variable is categorical and it is called class variable, in regression the variable is real–valued and is known as dependent variable. etc. Predictive models learn, using the
training data at hand, a mapping from the input variables to the dependent variable. The resulting model is then used to predict the value of the dependent variable for a new instance of which all the independent variables are known.
Examples of classification algorithms are: decision trees, neural network, nearest neighbor, rule-learners, etc..

 

==

these videos may be helpful:http://www.youtube.com/user/11AntsAnalyticsTV and there is also a free trial here:http://www.11antsanalytics.com/

 

==

Have you considered using JMP Pro, a tool from SAS that allows users to create predictive models visually?

 

 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

How could we do predictions with data mining for quant development?

How could we do predictions with data mining for quant development?

 

==

Predictive models can be grouped in two major groups: classification and regression models.
Both aim at building models that predicts the value of a variable knowing the values of other variables. Both models accept in input a set of training data. Each training instance has several attributes, one of which is the variable to be predicted. In classification, this variable is categorical and it is called class variable, in regression the variable is real–valued and is known as dependent variable. etc. Predictive models learn, using the
training data at hand, a mapping from the input variables to the dependent variable. The resulting model is then used to predict the value of the dependent variable for a new instance of which all the independent variables are known.
Examples of classification algorithms are: decision trees, neural network, nearest neighbor, rule-learners, etc..

 

==

these videos may be helpful:http://www.youtube.com/user/11AntsAnalyticsTV and there is also a free trial here:http://www.11antsanalytics.com/

 

 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

How could we do predictions with data mining with quant analytics?

How could we do predictions with data mining with quant analytics?

 

Predictive models can be grouped in two major groups: classification and regression models.
Both aim at building models that predicts the value of a variable knowing the values of other variables. Both models accept in input a set of training data. Each training instance has several attributes, one of which is the variable to be predicted. In classification, this variable is categorical and it is called class variable, in regression the variable is real–valued and is known as dependent variable. etc. Predictive models learn, using the
training data at hand, a mapping from the input variables to the dependent variable. The resulting model is then used to predict the value of the dependent variable for a new instance of which all the independent variables are known.
Examples of classification algorithms are: decision trees, neural network, nearest neighbor, rule-learners, etc..

 

 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Predictions for Wall Street in the year 2012 : Big Data processing and Analytics will be Big !

Predictions for Wall Street in the year 2012 : Big Data processing and Analytics will be Big !

http://www.wallstreetandtech.com/2012-outlook/big-data

Perfect time to this new subgroup – Conquering Big Data: the Final Frontier on Wall Street

http://www.linkedin.com/groups?about=&gid=4217767&trk=anet_ug_grppro

 

 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!