Category Archives: ensemble forecasts

ensemble forecasts, technology forecasting

Video Friday – Fracking

December 5, 2014 Clive Jones

Here is Brian Ellis from Michigan University Engineering with a look at the technology of hydraulic fracturing (fracking) and horizontal drilling – the innovations that recently pushed US oil production near the 10 million barrel per day mark.

I’m putting this up, rather than other, often excellent film clips showing people lighting water from their kitchen taps because the scale of shale oil and gas production has become so large. There really is a huge tradeoff between current employment and business activity and long term environmental effects.

Price, rather than environmental concerns, are likely to be the crucial factor in any scaleback.

At the same time, there is the possibility of further technical advance in the US shale oil and gas technologies, advances which may push extraction prices lower, giving the industry a longer lease during what may be a year or more of lower oil prices.

Fracking and its possible dynamics are critical to a lot of business activity and, thus, forecasting in the US.

accuracy of forecasts, artificial intelligence (AI), Bayesian methods, Big Data, bootstrapping, data science, energy forecasting, ensemble forecasts, predictive analytics

Global Energy Forecasting Competitions

June 18, 2014 Clive Jones

The 2012 Global Energy Forecasting Competition was organized by an IEEE Working Group to connect academic research and industry practice, promote analytics in engineering education, and prepare for forecasting challenges in the smart grid world. Participation was enhanced by alliance with Kaggle for the load forecasting track. There also was a second track for wind power forecasting.

Hundreds of people and many teams participated.

This year’s April/June issue of the International Journal of Forecasting (IJF) features research from the winners.

Before discussing the 2012 results, note that there’s going to be another competition – the Global Energy Forecasting Competition 2014 – scheduled for launch August 15 of this year. Professor Tao Hong, a key organizer, describes the expansion of scope,

GEFCom2014 (www.gefcom.org) will feature three major upgrades: 1) probabilistic forecasts in the form of predicted quantiles; 2) four tracks on demand, price, wind and solar; 3) rolling forecasts with incremental data update on weekly basis.

Results of the 2012 Competition

The IJF has an open source article on the competition. This features a couple of interesting tables about the methods in the load and wind power tracks (click to enlarge).

The error metric is WRMSE, standing for weighted root mean square error. One week ahead system (as opposed to zone) forecasts received the greatest weight. The top teams with respect to WRMSE were Quadrivio, CountingLab, James Lloyd, and Tololo (Électricité de France).

The top wind power forecasting teams were Leustagos, DuckTile, and MZ based on overall performance.

Innovations in Electric Power Load Forecasting

The IJF overview article pitches the hierarchical load forecasting problem as follows:

…participants were required to backcast and forecast hourly loads (in kW) for a US utility with 20 zones at both the zonal (20 series) and system (sum of the 20 zonal level series) levels, with a total of 21 series. We provided the participants with 4.5 years of hourly load and temperature history data, with eight non-consecutive weeks of load data removed. The backcasting task is to predict the loads of these eight weeks in the history, given actual temperatures, where the participants are permitted to use the entire history to backcast the loads. The forecasting task is to predict the loads for the week immediately after the 4.5 years of history without the actual temperatures or temperature forecasts being given. This is designed to mimic a short term load forecasting job, where the forecaster first builds a model using historical data, then develops the forecasts for the next few days.

One of the top entries is by a team from Électricité de France (EDF) and is written up under the title GEFCom2012: Electric load forecasting and backcasting with semi-parametric models.

This is behind the International Journal of Forecasting paywall at present, but some of the primary techniques can be studied in a slide set by Yannig Goulde.

This is an interesting deck because it maps key steps in using semi-parametric models and illustrates real world system power load or demand data, as in this exhibit of annual variation showing the trend over several years.

Or this exhibit showing annual variation.

What intrigues me about the EDF approach in the competition and, apparently, more generally in their actual load forecasting, is the use of splines and knots. I’ve seen this basic approach applied in other time series contexts, for example, to facilitate bagging estimates.

So these competitions seem to provide solid results which can be applied in a real-world setting.

Top image from Triple-Curve

accuracy of forecasts, ensemble forecasts, financial forecasting, forecasting research, stock market forecasts, time series forecasting

Stock Market Predictability – Controversy

May 5, 2014 Clive Jones

In the previous post, I drew from papers by Neeley, who is Vice President of the Federal Reserve Bank of St. Louis, David Rapach at St. Louis University and Goufu Zhou at Washington University in St. Louis.

These authors contribute two papers on the predictability of equity returns.

The earlier one – Forecasting the Equity Risk Premium: The Role of Technical Indicators – is coming out in Management Science. Of course, the survey article – Forecasting the Equity Risk Premium: The Role of Technical Indicators – is a chapter in the recent volume 2 of the Handbook of Forecasting.

I go through this rather laborious set of citations because it turns out that there is an underlying paper which provides the data for the research of these authors, but which comes to precisely the opposite conclusion –

The goal of our own article is to comprehensively re-examine the empirical evidence as of early 2006, evaluating each variable using the same methods (mostly, but not only, in linear models), time-periods, and estimation frequencies. The evidence suggests that most models are unstable or even spurious. Most models are no longer significant even insample (IS), and the few models that still are usually fail simple regression diagnostics.Most models have performed poorly for over 30 years IS. For many models, any earlier apparent statistical significance was often based exclusively on years up to and especially on the years of the Oil Shock of 1973–1975. Most models have poor out-of-sample (OOS) performance, but not in a way that merely suggests lower power than IS tests. They predict poorly late in the sample, not early in the sample. (For many variables, we have difficulty finding robust statistical significance even when they are examined only during their most favorable contiguous OOS sub-period.) Finally, the OOS performance is not only a useful model diagnostic for the IS regressions but also interesting in itself for an investor who had sought to use these models for market-timing. Our evidence suggests that the models would not have helped such an investor. Therefore, although it is possible to search for, to occasionally stumble upon, and then to defend some seemingly statistically significant models, we interpret our results to suggest that a healthy skepticism is appropriate when it comes to predicting the equity premium, at least as of early 2006. The models do not seem robust.

This is from Ivo Welch and Amit Goyal’s 2008 article A Comprehensive Look at The Empirical Performance of Equity Premium Prediction in the Review of Financial Studies which apparently won an award from that journal as the best paper for the year.

And, very importantly, the data for this whole discussion is available, with updates, from Amit Goyal’s site now at the University of Lausanne.

Where This Is Going

Currently, for me, this seems like a genuine controversy in the forecasting literature. And, as an aside, in writing this blog I’ve entertained the notion that maybe I am on the edge of a new form of or focus in journalism – namely stories about forecasting controversies. It’s kind of wonkish, but the issues can be really, really important.

I also have a “hands-on” philosophy, when it comes to this sort of information. I much rather explore actual data and run my own estimates, than pick through theoretical arguments.

So anyway, given that Goyal generously provides updated versions of the data series he and Welch originally used in their Review of Financial Studies article, there should be some opportunity to check this whole matter. After all, the estimation issues are not very difficult, insofar as the first level of argument relates primarily to the efficacy of simple bivariate regressions.

By the way, it’s really cool data.

Here is the book-to-market ratio, dating back to 1926.

But beyond these simple regressions that form a large part of the argument, there is another claim made by Neeley, Rapach, and Zhou which I take very seriously. And this is that – while a “kitchen sink” model with all, say, fourteen so-called macroeconomic variables does not outperform the benchmark, a principal components regression does.

This sounds really plausible.

Anyway, if readers have flagged updates to this controversy about the predictability of stock market returns, let me know. In addition to grubbing around with the data, I am searching for additional analysis of this point.

bagging, boosting, classification models, ensemble forecasts, random subspace ensemble methods

Random Subspace Ensemble Methods (Random Forest™ algorithm)

February 12, 2014 Clive Jones

As a term, random forests apparently is trademarked, which is, in a way, a shame because it is so evocative – random forests, for example, are comprised of a large number of different decision or regression trees, and so forth.

Whatever the name we use, however, the Random Forest™ algorithm is a powerful technique. Random subspace ensemble methods form the basis for several real world applications, such as Microsoft’s Kinect, facial recognition programs in cell phone and other digital cameras, and figure importantly in many Kaggle competitions, according to Jeremy Howard, formerly Kaggle Chief Scientist.

I assemble here a Howard talk from 2011 called “Getting In Shape For The Sport Of Data Science” and instructional videos from a data science course at the University of British Columbia (UBC). Watching these involves a time commitment, but it’s possible to let certain parts roll and then to skip ahead. Be sure and catch the last part of Howard’s talk, since he’s good at explaining random subspace ensemble methods, aka random forests.

It certainly helps me get up to speed to watch something, as opposed to reading papers on a fairly unfamiliar combination of set theory and statistics.

By way of introduction, the first step is to consider a decision tree. One of the UBC videos notes that decision trees faded from popularity some decades ago, but have come back with the emergence of ensemble methods.

So a decision tree is a graph which summarizes the classification of multi-dimensional points in some space, usually based on creating rectangular areas with reference to the coordinates. The videos make this clearer.

So this is nice, but decision trees of this sort tend to over-fit; they may not generalize very well. There are methods of “pruning” or simplification which can help generalization, but another tactic is to utilize ensemble methods. In other words, develop a bunch of decision trees classifying some set of multi-attribute items.

Random forests simply build such decision trees with a randomly selected group of attributes, subsets of the total attributes defining the items which need to be classified.

The idea is to build enough of these weak predictors and then average to arrive at a modal or “majority rule” classification.

Here’s the Howard talk.

Then, there is an introductory UBC video on decision trees

This video goes into detail on the method of constructing random forests.

Then the talk on random subspace ensemble applications.

Big Data, crowdsourcing analytics, data science, ensemble forecasts, prediction markets

Measuring the Intelligence of Crowds

January 19, 2014 Clive Jones

Researchers at Microsoft Research in the UK and Cambridge University report some fascinating and potentially useful results on crowdsourcing, based on a study of aggregating questions from a standard IQ test on Amazon’s Mechanical Turk (AMT).

The AMT site provides a place where workers can find problems that requesters have set up for crowdsourcing.

The introductory page to the site looks like this (click to enlarge).

So here’s an interesting way for people to make some money working from home, at their own hours, and yet stay busy. I’d like to look more deeply into this in a future post, but what these Crowd IQ researchers did is divvy up the questions from a widely utilized IQ test on the AMT site. They studied the effects of changing several parameters on their measures of Crowd IQ, but basically found that, with five or more reputable workers in a group, the Crowd IQ was usually higher than that of the individual workers in the group.

The Abstract for their 2012 study Crowd IQ: Measuring the Intelligence of Crowdsourcing Platforms describes the research and findings succinctly:

We measure crowdsourcing performance based on a standard IQ questionnaire, and examine Amazon’s Mechanical Turk (AMT) performance under different conditions. These include variations of the payment amount offered, the way incorrect responses affect workers’ reputations, threshold reputation scores of participating AMT workers, and the number of workers per task. We show that crowds composed of workers of high reputation achieve higher performance than low reputation crowds, and the effect of the amount of payment is non-monotone—both paying too much and too little affects performance. Furthermore, higher performance is achieved when the task is designed such that incorrect responses can decrease workers’ reputation scores. Using majority vote to aggregate multiple responses to the same task can significantly improve performance, which can be further boosted by dynamically allocating workers to tasks in order to break ties.

The IQ test is Raven’s Standard Progressive Matrices (SPM). If you want to take the test, look here.

SPM is a nonverbal, multiple-choice intelligence test based on the theory of general ability. The general setup is as in the following example.

Free riders are an interesting problem in a site like the Mechanical Turk. So, if people get paid by the number of correct answers, some simply select responses at random to maximize the speed at which they can put up answers. Because of this, AMT has a reputation mechanism indicating the expected quality of work of a worker, based on his or her past performance.

This research is has real-world implications. For example, increasing the payment for tasks too much results in actually diminuishing the quality of the answers, for a variety of reasons the authors consider.

The “workers” in this AMT-based study did not consult with each other about the answers, but were grouped into teams somehow by the researchers.

Here is a chart showing the increase in crowd IQ with the number of people in the group.

Here a HIT refers to a Human Intelligence Task.

Recommendations

First, experiment and monitor the performance. Our results suggest that relatively small changes to the parameters of the task may result in great changes in crowd performance. Changing parameters of the task (e.g. reward, time limits, reputation rage) and observing changes in performance may allow you to greatly increase performance. Second, make sure to threaten workers’ reputation by emphasizing that their solutions will be monitored and wrong responses rejected. Obviously, in a real-world setting it may be hard to detect free-riders without using a “gold-set” of test questions to which the requester already knows the correct response. However, designing and communicating HIT rejection conditions can discourage free riding or make it risky and more difficult. For instance, in the case of translation tasks requesters should determine what is not acceptable (e.g. using Google Translate) and may suggest that the response quality would be monitored and solutions of low quality would be rejected. Third, do not over-pay. Although the reward structure obviously depends on the task at hand and the expected amount of effort required to solve it, our results suggest that pricing affects not only the ability to s source enough workers to perform the task but also the quality of the obtained results. Higher rewards are likely to encourage a free-riding behavior and may affect the cognitive abilities of workers by increasing psychological pressure. Thus, for long term projects or tasks that are run repeatedly in a production environment, we believe it is worthwhile to experiment with the reward scheme in order to
find an optimum reward level. Fourth, aggregate multiple solutions to each HIT, preferably using an adaptive sourcing scheme. Even the simplest aggregation method – majority voting – has a potential to greatly improve the quality of the solution. In the context of more complicated tasks, e.g. translations, requesters may consider a two-stage design in which they first request several solutions, and then use another batch of workers to vote for the best one. Additionally, requesters may consider inspecting the responses provided by individuals that often disagree with the crowd – they might be coveted geniuses or free-riders deserving rejection.

Interesting stuff, and makes you want to try crowdsourcing.

bagging, Big Data, cell phone data analytics, data mining, data science, electric utility forecasting, ensemble forecasts, technology forecasting, utility load forecasting, Winters exponential smoothing

Analytics 2013 Conference in Florida

January 17, 2014 Clive Jones

Looking for case studies of data analytics or predictive analytics, or for Big Data applications?

You can hardly do better, on a first cut, than peruse the material now available from October’s Analytics 2013 Conference, held at the Hyatt Regency Hotel in Orlando, Florida.

Presented by SAS, dozens of presentations and posters from the Conference can be downloaded as zip files, unbundling as PDF files.

Download the conference presentations and poster presentations (.zip)

I also took an hour to look at the Keynote Presentation of Dr. Sven Crone of Lancaster University in the UK, now available on YouTube.

Crone, who also is affiliated with the Lancaster Centre for Forecasting, gave a Keynote which was, in places, fascinating, and technical and a little obscure elsewhere – worth watching if you time, or can run it in the background while you sort through your desk, for example.

A couple of slides caught my attention.

One segment gave concrete meaning to the explosion of data available to forecasters and analysts. For example, for electric power load forecasting, it used be the case that you had, perhaps, monthly total loads for the system or several of its parts, or perhaps daily system loads. Now, Crone notes the data to be modeled has increased by orders of magnitude, for example, with Smart Meters recording customer demand at fifteen minute intervals.

Another part of Crone’s talk which grabbed my attention was his discussion of forecasting techniques employed by 300 large manufacturing concerns, some apparently multinational in scale. The following graph – which is definitely obscure by virtue of its use of acronyms for types of forecasting systems, like SOP for Sales and Operation Planning – highlights that almost no company uses anything except the simplest methods for forecasting, relying largely on judgmental approaches. This aligns with a survey I once did which found almost no utilities used anything except the simplest per capita forecasting approaches. Perhaps things have changed now.

Crone suggests relying strictly on judgment becomes sort of silly in the face of the explosion of information now available to management.

Another theme Crone spins in an amusing, graphic way is that the workhorses of business forecasting, such as exponential smoothing, are really products from many decades ago. He uses funny pics of old business/office environments, asking whether this characterizes your business today.

The analytic meat of the presentation comes with exposition of bagging and boosting, as well as creative uses for k-means clustering in time series analysis.

At which point he descends into a technical wonderland of complexity.

Incidentally, Analytics 2014 is scheduled for Frankfurt, Germany June 4-5 this coming Spring.

Watch here for my follow-on post on boosting time series.

Business Forecasting