Romano Associates

Romano Associates

Extracting Knowledge From Data

There are three key approaches to extracting Knowledge from Data:

Deep Learning – Using NEURAL NETWORK and MACHINE LEARNING algorithms to capture knowledge hidden within BIG DATA.

Forecasting – Using past data to make the best possible estimate of a future outcome. ARIMA based techniques are excellent for this.

Simulation – Using what you already know about the way elements of a problem interact to make a probability based estimation of future outcomes. Monte Carlo Simulation is excellent for this.

Quite often the optimum solution will involve hybrid models using all three technologies.

AI & Deep Learning

AI and Deep Learning is the current name for a collection of computational techniques that have emerged from Data Science labs since the early 1980’s.The first generation of tools, such as OPS5, embodied the concept of Non-Prodedural Programming and Expert Systems. These tools are excellent for problems involving configuration, but of limited applicability to wider domains.

The second generation of tools emerged in the 1990’s. These tools were more useful for general problem solving and form the basis of many of today’s advanced Data Science workbench tools such as KNIME.

The second generation of tools were of two “flavours”, Rule Induction tools such as C4.5 and Artificial Neural Networks. These tools, though conceptually brilliant, were often of limited in practical use until today’s powerful compute platforms became available.

Deep Learning represents the current generation of Artificial Neural Network tools as developed and used by such organisations as Amazon, Google and Microsoft to power their analytical platforms at scale.

Though these tools are extremely effective they can require very special hardware platforms to run on and it is often only feasible to develop these models on workstation class hardware before  transferring them to a production environment such as AWS.

Even at AWS scale some problem domains are beyond today’s compute platforms but may become practical as Quantum Computers become available.

ARIMA Forecasting

ARIMA – Auto-Regressive Integrated Moving Average

These models are fitted to time series data to better understand the data and to predict future outcomes (values) in the time series.

Examples of use would be to predict:

Call Center Loading

Seasonal Sales Demand

Short-term Exchange Rate Movements

For those with a technical interest in the ARIMA technique go to the following links:

MIT ARIMA Time Series Forecasting Video Course Module

ARIMA Theoretical Definition

Examples

Investment Analysis – Investment Allocation Optimization

Budget Optimization for Social Media Advertising and PPC – Budget Optimization

Social Media ClusteringClustering Social Media Users by Authority

ForecastingFX Forecast of EUR vs GBP

Data Analysis – Hans Rossling video – Let my data change your mindset

Big Data – Kenneth Cukier video – Big data is better data

Data Visualisation – David McCandless video – The beauty of data visualisation

Data Analytics – Susan Etlinger video – What do we do with all this data

Data Mining – Shyam Sankar video – The rise of human-computer cooperation

David J Romano Associates

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