Preface

Predicting is making claims about something that will happen, often based on information from past and from current state.

Everyone solves the problem of prediction every day with various degrees of success. For example weather, harvest, energy consumption, movements of forex (foreign exchange) currency pairs or of shares of stocks, earthquakes, and a lot of other stuff needs to be predicted. ...

Predictive Analytics

With classification, deep learning is able to establish correlations between, say, pixels in an image and the name of a person. You might call this a static prediction. By the same token, exposed to enough of the right data, deep learning is able to establish correlations between present events and future events. The future event is like the label in a sense. Deep learning doesn’t necessarily care about time, or the fact that something hasn’t happened yet. Given a time series, deep learning may read a string of number and predict the number most likely to occur next.

dataVs:

using Flash

Predictions

using fbProphet:

todos:

streamlit: https://docs.streamlit.io/en/stable/api.html#display-data

plotly:https://plotly.com/python/time-series/

Live Demos

https://yangboz.github.io/labs/lp/LotteryPrediction_AmCharts_R.swf https://yangboz.github.io/labs/lp/LotteryPrediction_AmCharts_RCX.swf https://yangboz.github.io/labs/lp/LotteryPrediction_FlexCharts.swf

notes

besides of following "law of proability","Probability: Independent Events", there are still "Saying "a Tail is due", or "just one more go, my luck is due to change" is called The Gambler's Fallacy" existed.

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Refs:

http://deeplearning4j.org/usingrnns.html

http://www.scriptol.com/programming/list-algorithms.php

http://www.ipedr.com/vol25/54-ICEME2011-N20032.pdf

http://www.brightpointinc.com/flexdemos/chartslicer/chartslicersample.html

http://stats.stackexchange.com/questions/68662/using-deep-learning-for-time-series-prediction

Python logutils

Python data analysis_pandas

Python data minning_orange

Python data-mining and pattern recognition packages

Python Machine Learning Packages

Conference on 100 YEARS OF ALAN TURING AND 20 YEARS OF SLAIS

USA Draft Lottery 1970

Python Scikit-Learn

Python Multivarite Pattern Analysis

BigML

Patsy

StatModel

Neural Lotto — Lottery Drawing Predicting Method

Random.org

Predictive Analytics Guide

[TensorFlow Tutorial for Time Series Prediction:] (https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series)

Roadmap:

landing page:

PoC

API public service:

Phase I.Graphics: Looking at Data;

1.A single variable:Shape and Distribution; ( Dot/Jitter plots,Histograms and Kernel Density Estimates,Cumulative Distribution Function,Rank-Order...)

2.Two variables:Establishing Relationships; ( Scatter plots,Conquering Noise,Logarithmic Plots,Banking...)

3.Time as a variable: Time-Series Analysis; (Smoothing,Correlation,Filters,Convolutions..)

4.More than two variables;Graphical Multivariate Analysis;(False-color Plots,Multi plots...)

5.Intermezzo:A Data Analysis Session;(Session,gnuplot..)

6...

Phase II.Analytics: Modeling Data;

1.Guesstimation and the back of envelope;

2.Models from scaling arguments;

3.Arguments from probability models;

4...

Phase III.Computation: Mining Data;

1.Simulations;

2.Find clusters;

3.Seeing the forest for the decision trees;

4....

Phase IV.Applications: Using Data;

1.Reporting, BI (Business Intelligence),Dashboard;

2.Financial calculations and modeling;

3.Predictive analytics;

4....

=======

Draft plan

Phase I.Graphics: Looking at Data;

1.A single variable:Shape and Distribution; ( Dot/Jitter plots,Histograms and Kernel Density Estimates,Cumulative Distribution Function,Rank-Order...)

2.Two variables:Establishing Relationships; ( Scatter plots,Conquering Noise,Logarithmic Plots,Banking...)

3.Time as a variable: Time-Series Analysis; (Smoothing,Correlation,Filters,Convolutions..)

4.More than two variables;Graphical Multivariate Analysis;(False-color Plots,Multi plots...)

5.Intermezzo:A Data Analysis Session;(Session,gnuplot..)

6...

Phase II.Analytics: Modeling Data;

1.Guesstimation and the back of envelope;

2.Models from scaling arguments;

3.Arguments from probability models;

4...

Phase III.Computation: Mining Data;

1.Simulations;

2.Find clusters;

3.Seeing the forest for the decision trees;

4....

Phase IV.Applications: Using Data;

1.Reporting, BI (Business Intelligence),Dashboard;

2.Financial calculations and modeling;

3.Predictive analytics;

4....

TODO:

1.Using D3JS Chart to visualization the data; (http://techslides.com/over-1000-d3-js-examples-and-demos/);

2.Using Tableau software trail and error; (http://www.tableausoftware.com/)

3.Using PredictionIO API; (http://prediction.io)

4.Using Free Tempo-DB for time series database storage ; (https://tempo-db.com/docs/batch-import/python-script/)

5.Using BigML API; (https://bigml.com/)

6.Pandas+scikit+matplotlib+IPython Notebook; (http://nbviewer.ipython.org/url/www.onewinner.me/en/devoxxML.ipynb)

7.Implementing a highly scalable prediction; (http://www.slideshare.net/SpringCentral/implementing-a-highly-scalable-stock-prediction-system-with-r-apache-geode-and-spring-xd) (https://github.com/Pivotal-Open-Source-Hub/StockInference-Spark)

fbprophet: https://facebook.github.io/prophet/docs/quick_start.html

References

TensorFlow Tutorial for Time Series Prediction: https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series

papers

https://www.datascience.us/predicting-success-in-lottery-with-deep-learning/

Commercial support and training

Commercial support and training is available , please mailto [email protected]