According to Intensity’s latest US recession start date forecast, there is a 50% probability of a recession starting sometime in the January to February 2019 period.  And a 97% probability of it starting sometime within the next 6 months.

Their “point estimate” of a recession start is January 2019.

Like, as in, right now!

If true, it will take awhile for the impacts to start showing up in the official government statistics.  But the stock market sell-off last quarter may be a harbinger of things to come.

Intensity, an economics and data science firm based in San Diego, CA, developed and back-tested a machine learning prediction algorithm for its clients.  The firm started releasing a monthly forecast of the next US recession start date to the public starting in March 2018.

Over the course of the last 11 months, it has been interesting following the updates to their forecast as economic conditions changed.

Intuitively, one would expect that the forecast would “settle down”, the closer the expected start date became.

And it got me thinking about what the best way is to visualize these changing forecasts.

Visualizing Forecast Updates Over Time

The forecasted recession start date is not linear with time.  For example, in March 2018, the next recession was forecasted by Intensity to start in April 2019.  But in April 2018, the forecast was revised, and the recession was to start 6 months earlier in October 2018.

Plotting the month of the forecast on the x-axis and the forecasted month of the recession start on the y-axis yields a “traditional time series” view as shown below.

As time progresses from left to right, we can see the forecasted recession start date fluctuating up and down, settling on January 2019, the most recent forecasted start date.

However, another way to visualize this is to show the progression of time vertically, from bottom to top.  In this case the forecasted recession start date would fluctuate horizontally, left and right, as shown below.

I don’t know about you, but I find this second view more appealing.  Maybe it is the old economist in me, trained on the Phillips Curve in graduate school.  But for me, the vertical, “up-down” orientation makes the variation in the forecasted recession start date “pop” more than in the horizontal, “left-to-right” view.

So, Recession in 2019?

It will be very interesting to see if Intensity sticks to its January 2019 point estimate.  Prior to the unexpectedly positive December 2018 jobs report, the consensus seemed to be a recession starting some time in 2019 or 2020.  For example, Gary Shilling recently tossed his hat into the recession ring with a predicted 66% chance of a recession in 2019.

However, the positive jobs report apparently has many economists now softening their stance on a recession this year.  And there is talk of policy makers being able to sidestep a recession.

Only time will tell…so stay tuned!

Plotting Ordered Times Series in Tableau

By the way, these charts were made in Tableau.  And it was not as straight forward as flipping the axes to get the vertical view.  Tableau’s default inclination is to “connect the dots” from left to right when time is involved.

Fortunately, there is an easy way to get Tableau to connect the dots vertically.  This makes use of the Path property in the Marks card.  I simply added a field to my raw data that indicated the order of my data, which, of course was calendar order.

Then dropping this field on the Path property in the Marks card tells Tableau to connect the dots (or “Marks” in Tableau-speak) in this order.  With the date of the forecast on the vertical, y-axis, Tableau connects the dots from bottom to top.

Very slick!

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(*It’s about the analytics.)

Machine Learning. Deep Learning. Data Science. Artificial Intelligence. Big Data.

Not a day goes by that one or all of these buzzwords stream past in our business news feeds.

Data analytics has become mainstream. And you better jump on board or risk being left at the station!

Just within the last year or so, searches of these topics have taken off. In fact, according to Google, in early 2017, search interest in one of these topics, machine learning, has eclipsed that of big data:

So, how do time series methods for forecasting fit into the taxonomy that currently defines the data science field?

Data science taxonomy

Key data science terms that are related to time series methods for forecasting are data mining, predictive analytics, machine learning (supervised and unsupervised), regression, structured and unstructured data.

These are not necessarily mutually exclusive. At the risk of incurring the wrath of the data science gods, here is our simplification:

Structured vs. unstructured data

Structured data are organized into “rows and columns” (spreadsheet); unstructured data are not (text in a book).

Time series methods use structured data.

Data mining

Data mining seeks to find patterns in data, whether structured or unstructured.

Time series methods seek to find patterns that repeat over time.

Predictive analytics

Predictive analytics seeks to find a relationship between a variable of interest (e.g. customer churn) and multiple dimensions (e.g. age, length of contract, zip code). These dimensions can be used to predict the likelihood of a customer churning (in our example).

Typically, predictive analytics is not based on time series data but “cross-sectional” data like a customer set. Additionally, time series methods use only a very limited set of dimensions, the primary one being past behavior of the variable being forecasted (e.g. sales).

Time series methods typically use the past behavior of the variable being forecasted as the primary dimension.

Machine learning

Machine learning means that a computer is using a program (algorithm) to “connect the dots” in the data. If you run a regression model in Excel you are engaging in machine learning.

However, supervised machine learning does not mean you are keeping watch over Excel as it does its stuff!

This is NOT what “supervised” machine learning means!

Supervised machine learning means that the computer is seeking to find a relationship between a single variable (e.g. churn) and many dimensional variables (e.g. age, length of contract, zip code).

Unsupervised machine learning means that the computer is seeking to find a relationship between many dimensions (e.g. age, length of contract, zip code) so that customers can, for example, be clustered into a small number of groups or tribes with similar characteristics.

Time series methods are a type of supervised machine learning since they attempt to find a relationship between present and past behavior.

Regression

Regression is one way a machine finds relationships between a single variable and a few (or many) dimensional variables or past values of the variable itself. There are several flavors of regression.

Time series models typically use least squares regression or maximum likelihood.

Bottom line

So, when you use time series methods for forecasting you are probably mining structured data using supervised, regression- or maximum likelihood-based, machine learning.

Part 1 – Practical Time Series Forecasting – Introduction

Part 2 – Practical Time Series Forecasting – Some Basics

Part 3 – Practical Time Series Forecasting – Potentially Useful Models

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At Boulder Equity Analytics (BEA), “Our mission is to build an intelligent, enriched and fully interactive database from all the publicly available reports to improve the productivity and insight of the analyst covering an industry sector.”  We call it O.A.K.L.E.Y, collaborative artificial intelligence, a 2nd order of intelligence.  And we’re now creating the services around it.

Convenient, comparative, contextual and interactive services like ours will provide a foundation and no doubt be successful.  But that’s not our end goal.  

Competitive

We build tools for competitive analysis.  But more importantly we want to build tools that help you compete, in your job and in your markets. We can’t stop with a better version of what you can get elsewhere.  While there might be a market for a less expensive Bloomberg, what’s the competitive differentiator for you?  You have the same service as the rest of the market.  Which brings us to an interesting dilemma.  If we build a great service, after the early adopters exploit the advantage, everyone is equal again.

This is where our AI needs to augment your intelligence, not supply its own.

1st order intelligence

With our first products we’re using AI to “read” the market and company information so you can be more efficient in your research. This is the 1st order of intelligence.  It is like going to school to learn finance and then working the back room reading and reporting on company results.  It is the foundation for the next step in your career, using that knowledge to make investment decisions.

As the impact of MIFID II hits, our products will help you build a bigger and faster knowledge base with no bias and 100% recall at web speed and scale. Our products don’t get tired. That alone will help you beat the market.

But here’s where most take the easy way out: focus on speed and trades, not investing.  High-frequency trading (HFT) uses AI  It is in fact one of the most pervasive and advanced AIs in the world, second only to computer viruses and cyber bots.

This is a common execution of an AI strategy.  Go straight from the research department to the 3rd order of intelligence, an AI making autonomous decisions.  Think IBM Watson.  No humans required.

We think that’s the wrong approach. Black boxes powered by AI are as risky as they are powerful.

2nd order intelligence

Our goal is to deliver collaborative AI, a 2nd order of machine intelligence.  Our soon to be announced Boulder Earnings Call Tracker (BECT) for Investor Relations, Analysts, Portfolio Managers and Independent Research is our first offering at the next level.  

Simply, the 2nd order is collaborative and pulls out multiple dimensions of sentiment and tone built with new analytics methods that provide the nuance missing from today’s offerings.  It enables you to create completely new strategies once you’ve been able to look at your markets and competitors at a deeper level and at machine speed and scale.

In the past, long term was long term and even when you were right, it may have been years before the market rewarded you for your positions. Information was created, reported and analyzed from the paradigm of quarterly reports and press releases.  Your technology, economic and geopolitical models were relatively fixed.  Sectors followed predictable patterns.

We think that has changed.  

Markets have moved to the “second half of the chessboard” where exponential changes will create and destroy large enterprises (wealth) in quarters not decades.  What are considered fundamental long term investment decisions today, will look like day trades in the future.

Our goal is to bring the 2nd order of intelligence to you, providing a foundation for contextual decisions.  This will empower you with individualized tools to compete and win, whether against a machine or another trader. 

You won’t be able to read fast enough.  You need to retool.  And the first ones to do it will win.  In the immortal words of Walter Gretzky, as passed on through his son Wayne: “Skate to where the puck is going, not where it has been.” 

Tom 

@tom_semantic

KDD Analytics and Boulder Equity Analytics are partnering to deliver collaborative artificial intelligence to the financial and competitive analysis industries.

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AI, artificial intelligence, has been through several boom and bust cycles.  Today the pronouncements are everywhere with AI coming soon to everything from medicine to your underwear.  For those of us laboring in the dark for years, it feels good to be the most popular kid on the block.  Warning!  What the marketing gods grant, they can take away overnight.

 I have been lucky to work with AI technology in language for the last six years.  As we worked to find traction, I was exposed to a huge variety of application problems.  Now focused on financial reporting and compliance, my favorite inbound call was from a fund in the UK with $11B AUM.  

“We have been reading about all the wonderful things AI could do and we want to purchase some ‘AI’”. 

They did not have a data repository, everything was on analyst PCs and not the cloud and they had no analytics capability.  While we completed a small proof of concept, it failed to get approval because “our analysts say they already read everything anyway”.  Could have seen that coming (good news is that it was part of the inspiration to start Boulder Equity Analytics).

AI is not just software

If you’re in the field, you’ve probably run into the same request, either from a prospective customer or from your own management.  Maybe your marketing department has already asked you for some AI to put into a product or, worse yet, added to the website. Now you’re supposed to figure out what sort of machine learning algorithm you can put in the product.  It’s just software, right?

This feels like the ’90s when the internet came online and everyone had to have an internet “strategy”.  Executives that didn’t even know how to turn on a PC were jumping on the bandwagon; most thought a website was an internet strategy.  

Keep it grounded

My request to AI practitioners is to keep it grounded and fight back. Don’t be co-opted into the marketing BS and don’t let management that has no experience with AI try to build an “AI” strategy from a 15 minute YouTube session. Read the technical experts that are debunking the exaggerated claims (like this piece by Yoav Goldberg) so you know the real state of the field. Talk to experts and ask questions, challenge them on the fail modes, the testing and the risks.

Walk before you run

If you’re in management, know that this is going to change your life, your organization, your markets and your customers.  The claims are real but do your homework.  Implementing an “AI” strategy is not a software project or something you can buy.  You need to start small, build a small team and execute some very small projects.  You do not need a big budget but you do need AI technology, domain expertise, access to the data and good UX people. Before attempting this, you should already have a good analytics foundation and culture (at least in the business you’re starting with).  

That said, don’t hold back.  Everything you’ve read about the exponential nature of the progress and its impact is true and coming fast.  The impact will be personal. If you don’t start right now, you will get run over by the freight train that is AI powered innovation. 

Tom (@tom_semantic)

KDD Analytics and Boulder Equity Analytics are partnering to deliver collaborative artificial intelligence to the financial and competitive analysis industries.

The post Buzzword Overkill – AI is Not a Thing People, it’s a Discipline appeared first on KDD Analytics.