Trend Forecasting Quotes

Extrapolations are useful, particularly in that form of soothsaying called forecasting trends. But in looking at the figures or charts made from them, it is necessary to remember one thing constantly: The trend-to-now may be a fact, but the future trend represents no more than an educated guess. Implicit in it is "everything else being equal" and "present trends continuing." And somehow everything else refuses to remain equal, else life would be dull indeed.

guesstimate = better than a guess but not as guaranteed as an estimate... i.e. It's simply a calculated forecast based on probability, historical trends, observations, analytical research, politics, studies of human nature and good ol' common sense (the latter 2 of which usually cause a toxic sediment when mixed, LOL)...

Futurists are skilled at listening to and interpreting the signals talking. It’s a learnable skill, and a process anyone can master. Futurists look for early patterns—pre-trends, if you will—as the scattered points on the fringe converge and begin moving toward the mainstream. They know most patterns will come to nothing, and so they watch and wait and test the patterns to find those few that will evolve into genuine trends. Each trend is a looking glass into the future, a way to see over time’s horizon. The advantage of forecasting the future in this way is obvious. Organizations that can see trends early enough to take action have first-mover influence. But they can also help to inform and shape the broader context, conversing and collaborating with those in other fields to plan ahead.

So these are the possibilities I see with regard to economic forecasts: Most economic forecasts are just extrapolations. Extrapolations are usually correct but not valuable. Unconventional forecasts of significant deviation from trend would be very valuable if they were correct, but usually they aren’t. Thus most forecasts of deviation from trend are incorrect and also not valuable. A few forecasts of significant deviation turn out to be correct and valuable—leading their authors to be lionized for their acumen—but it’s hard to know in advance which will be the few right ones. Since the overall batting average with regard to them is low, unconventional forecasts can’t be valuable on balance. There are forecasters who became famous for a single dramatic correct call, but the majority of their forecasts weren’t worth following.

Search engine query data is not the product of a designed statistical experiment and finding a way to meaningfully analyse such data and extract useful knowledge is a new and challenging field that would benefit from collaboration. For the 2012–13 flu season, Google made significant changes to its algorithms and started to use a relatively new mathematical technique called Elasticnet, which provides a rigorous means of selecting and reducing the number of predictors required. In 2011, Google launched a similar program for tracking Dengue fever, but they are no longer publishing predictions and, in 2015, Google Flu Trends was withdrawn. They are, however, now sharing their data with academic researchers... Google Flu Trends, one of the earlier attempts at using big data for epidemic prediction, provided useful insights to researchers who came after them... The Delphi Research Group at Carnegie Mellon University won the CDC’s challenge to ‘Predict the Flu’ in both 2014–15 and 2015–16 for the most accurate forecasters. The group successfully used data from Google, Twitter, and Wikipedia for monitoring flu outbreaks.

There is a second type of technical analysis that neither predicts or forecasts. This type is based on reacting to price action, as trend trader Martin Estlander notes: “We identify market trends, we do not predict them. Our models are kept reactive at all times.

But if we have carefully chosen indicators that characterize an administrative unit and watch them closely, we are ready to apply the methods of factory control to administrative work. We can use de facto standards, inferred from the trend data, to forecast the number of people needed to accomplish various anticipated tasks. By rigorous application of the principles of forecasting, manpower can be reassigned from one area to another, and the headcount made to match the forecasted growth or decline in administrative activity. Without rigor, the staffing of administrative units would always be left at its highest level and, given Parkinson’s famous law, people would find ways to let whatever they’re doing fill the time available for its completion.

Less science-curious adults were like hedgehogs: they became more resistant to contrary evidence and more politically polarized as they gained subject matter knowledge. Those who were high in science curiosity bucked that trend. Their foxy hunt for information was like a literal fox’s hunt for prey: roam freely, listen carefully, and consume omnivorously. Just as Tetlock says of the best forecasters, it is not what they think, but how they think. The best forecasters are high in active open-mindedness. They are also extremely curious, and don’t merely consider contrary ideas, they proactively cross disciplines looking for them. “Depth can be inadequate without breadth,” wrote Jonathan Baron, the psychologist who developed measurements of active open-mindedness.

Are we expanding our sales force appropriately to match needed sales growth and market penetration?   2. Are our reps properly trained, and what is the lag time between training and an effective rep?   3. Is our compensation package and awards program sufficient to attract and retain high performers?   4. Is our field sales forecasting system functioning properly to anticipate negative trends?   5. Can we continue to leverage the sales expense line without damaging sales?   6. Is our expense budget tracking system effective?   7. Are we accurately monitoring sales force morale?   8. Is our pay schedule competitive?

Once I saw this trend, the paper quickly wrote itself and was titled “Has Financial Development Made the World Riskier?” As the Wall Street Journal reported in 2009 in an article on my Jackson Hole presentation: Incentives were horribly skewed in the financial sector, with workers reaping rich rewards for making money but being only lightly penalized for losses, Mr. Rajan argued. That encouraged financial firms to invest in complex products, with potentially big payoffs, which could on occasion fail spectacularly. He pointed to “credit default swaps” which act as insurance against bond defaults. He said insurers and others were generating big returns selling these swaps with the appearance of taking on little risk, even though the pain could be immense if defaults actually occurred. Mr. Rajan also argued that because banks were holding a portion of the credit securities they created on their books, if those securities ran into trouble, the banking system itself would be at risk. Banks would lose confidence in one another, he said. “The inter-bank market could freeze up, and one could well have a full-blown financial crisis.” Two years later, that’s essentially what happened.2 Forecasting at that time did not require tremendous prescience: all I did was connect the dots using theoretical frameworks that my colleagues and I had developed. I did not, however, foresee the reaction from the normally polite conference audience. I exaggerate only a bit when I say I felt like an early Christian who had wandered into a convention of half-starved lions. As I walked away from the podium after being roundly criticized by a number of luminaries (with a few notable exceptions), I felt some unease. It was not caused by the criticism itself, for one develops a thick skin after years of lively debate in faculty seminars: if you took everything the audience said to heart, you would never publish anything. Rather it was because the critics seemed to be ignoring what was going on before their eyes.

To realize Government 3.0, focusing on the people through opening, sharing, and cooperation, the ACRC strived to resolve the inconveniences of the people by preventing complaints in advance by preemptively coming up with countermeasures and providing complaint trend forecast services

The research director at Fairchild Semiconductor Co., a brilliant engineer named Gordon Moore, contributed a four-page piece insouciantly entitled “Cramming More Components onto Integrated Circuits.” The essay forecast that as circuits became more densely packed with microscopic transistors, computing power would exponentially increase in performance and diminish in cost over the years. Moore contended that this trend could be predicted mathematically, so that memory costing $500,000 in 1965 would come all the way down to $3,000 by 1985—an insight so basic to the subsequent growth and expansion of the computer industry that ever since then it has been known as “Moore’s Law.

Some analysts have renamed the welfare state, which obtained basically from about 1945 until in the 1970s, the garrison state. State legitimacy now depends on protection from these threats by targeting of dangerous others. I’ll say more about this in two weeks, but just to repeat what I indicated last week, the idea that the globalized form of capitalism means that decisions about the economic security and welfare of citizens are no longer within the hands necessarily of nation-state governors. To preserve their legitimacy as governors, they need to find a new basis for legitimation. Some people are arguing, and I would agree with much of this, that this is the new basis. The protection from dangerous others. We have endless enemies. Foreign communism morphed into terrorism. We now have a tremendous fear of immigrants and refugees. Witness the recent ban orders, the deportations, the detentions, the demonization of others. We have domestic enemies, people of color, the young, the old, LGBTQ communities, the differently abled, and along with that the militarization of the police and the criminalization of protest, which we’ll talk about in the last couple of weeks. Where is all of this headed? The Pentagon has a very bleak view of the future (see “Megacities: Urban Future, the Emerging Complexity: A Pentagon Video”), which views urban areas (both foreign and domestic) as basically breeding grounds for instability, unrest, and chaos. To think about the kind of underlying view of humanity this way I think comes naturally in some sense out of this very long history of militarization. That is, if you think of yourself as military, then everybody outside is an enemy. This is also what becomes part of the problem of militarizing the police. As the police become increasingly militaristic, the people that they supposedly protect and serve begin to look more and more like the non-police, like the enemy. This is, I think, an extremely dangerous kind of trend that we’re seeing. The forecast that this is the way in which the military will sort of reproduce itself by now being able to respond to these kinds of future threats where the mass of humanity is either an enemy or is in a witting or unwitting cloak for enemies. It’s extremely dangerous. One we should think very carefully about, but this is the Pentagon’s view largely of what that future looks like, and it is, in fact, urban, militarized, and dangerous.

Human civilization has in the past absorbed similar technology-driven shocks to the economy, turning hundreds of millions of farmers into factory workers over the nineteenth and twentieth centuries. But none of these changes ever arrived as quickly as AI. Based on the current trends in technology advancement and adoption, I predict that within fifteen years, artificial intelligence will technically be able to replace around 40 to 50 percent of jobs in the United States. Actual job losses may end up lagging those technical capabilities by an additional decade, but I forecast that the disruption to job markets will be very real, very large, and coming soon.

The forecasts that are potentially valuable are those that correctly foresee deviation from long-term trends and recent levels. If a forecaster makes a non-conforming, non-extrapolation prediction that turns out to be correct, the outcome is likely to come as a surprise to the other market participants. When they scramble to adjust their holdings to reflect it, the result is likely to be gains for the few who correctly foresaw it. There’s only one catch: since major deviations from trend (a) occur infrequently and (b) are hard to correctly predict, most unconventional, non-extrapolation forecasts turn out to be incorrect, and anyone who invests on their basis is usually likely to do below average.

nowhere has the stagger chart been more productive than in forecasting economic trends. The way it works is shown in the figure below, which gives us forecasted rates of incoming orders for an Intel division. The stagger chart then provides the same forecast prepared in the following month, in the month after that, and so on. Such a chart shows not only your outlook for business month by month but also how your outlook varied from one month to the next. This way of looking at incoming business, of course, makes whoever does the forecasting take his task very seriously, because he knows that his forecast for any given month will be routinely compared with future forecasts and eventually with the actual result. But even more important, the improvement or deterioration of the forecasted outlook from one month to the next provides the most valuable indicator of business trends that I have ever seen.

Most economic forecasts consist of extrapolations of current levels and long-term trends. And since the economy usually doesn’t depart much from those levels and trends, most extrapolation forecasts turn out to be correct. But those extrapolation forecasts are likely to be commonly shared, already reflected in the market prices for assets, and thus not generators of superior performance—even when they come true. Here’s how Nobel Prize–winning economist Milton Friedman put it:

The Federal Reserve is not currently forecasting a recession. Ben Bernanke 
January 10, 2008

The forecasts that are potentially valuable are those that correctly foresee deviation from long-term trends and recent levels. If a forecaster makes a non-conforming, non-extrapolation prediction that turns out to be correct, the outcome is likely to come as a surprise to the other market participants.

THE FIRST STEP of forecasting the future requires a trip to the fringes of science, technology, design, and society, to where unusual experimentation is taking place. For it’s at the fringe that all trends are born.

The duality dilemma was responsible for a lack of forward-thinking at BlackBerry, which never had an executable plan to remake the phone’s form factor and operating system. Right-brained staff wanted to make serious changes to the phone, while left-brained staff were fixated on risk and maintaining BlackBerry’s customer base. The future of the company hinged on its ability to bring both forces together to forecast trends and plan for the future.

The duality dilemma was responsible for a lack of forward-thinking at BlackBerry, which never had an executable plan to remake the phone’s form factor and operating system. Right-brained staff wanted to make serious changes to the phone, while left-brained staff were fixated on risk and maintaining BlackBerry’s customer base. The future of the company hinged on its ability to bring both forces together to forecast trends and plan for the future. Overcoming the duality dilemma is possible, and it’s a matter of highlighting—rather than discouraging or downplaying—the strengths of both sides. The Hasso Plattner Institute of Design at Stanford University (also known as “the d.school”) teaches a practice that addresses the duality dilemma and illuminates how an organization can harness both strengths in equal measure, alternately broadening (“ flaring”) or narrowing (“ focusing”) its thinking. 2

THE FINAL STEP of our forecasting process is to pressure-test any strategy created to address a technology trend. Scenarios, as we’ve seen, help inform strategy; they fill in the necessary details in order to tell a complete story. However, in our zeal to pursue what’s new and what’s next, critical questions and details can be overlooked.

Regardless of the industry or circumstances, one forecast has always been right throughout history: technology will advance, it will invariably intersect with other sources of change within society, and trends are the signposts showing us how changes will manifest in real life.

We misidentify trends (or miss them altogether) when we focus exclusively on technology, when the other factors in play are seemingly unrelated, or when the adjacent sources of change aren’t part of a compelling narrative. Forecasting the future doesn’t always yield headline-worthy results, even if certain trends promise to change how we live on this planet.

Amazon’s founder, Jeff Bezos, has said that when mapping the future, leaders must “be stubborn on vision but flexible on details.”1 You may have correctly forecast a new trend and set a course of action; now, you must explore the particulars of that course, looking for possible roadblocks.

Forecasting the future is a matter of listening to, recognizing, and acting on signals. Every step isn’t entirely about logic and data—the process also requires active dreaming and creativity. Rather than prophesying, seeing trends is a matter of looking for emerging changes at the fringe, within organizations, and in our societies. It cannot be relegated to inventive visionaries, nor can it be mapped entirely by left-brain thinkers. Great trends forecasting unites opposing forces, harnessing both wild imagination and pragmatism.

1. Find the Fringe: Cast a wide enough net to harness information from the fringe. This involves creating a map showing nodes and the relationships between them, and rounding up what you will later refer to as “the unusual suspects.” 2. Use CIPHER: Uncover hidden patterns by categorizing data from the fringe. Patterns indicate a trend, so you’ll do an exhaustive search for Contradictions, Inflections, Practices, Hacks, Extremes, and Rarities. 3. Ask the Right Questions: Determine whether a pattern really is a trend. You will be tempted to stop looking once you’ve spotted a pattern, but you will soon learn that creating counterarguments is an essential part of the forecasting process, even though most forecasters never force themselves to poke holes into every single assumption and assertion they make. 4. Calculate the ETA: Interpret the trend and ensure that the timing is right. This isn’t just about finding a typical S-curve and the point of inflection. As technology trends move along their trajectory, there are two forces in play—internal developments within tech companies, and external developments within the government, adjacent businesses, and the like—and both must be calculated. 5. Create Scenarios and Strategies: Build scenarios to create probable, plausible, and possible futures and accompanying strategies. This step requires thinking about both the timeline of a technology’s development and your emotional reactions to all of the outcomes. You’ll give each scenario a score, and based on your analysis, you will create a corresponding strategy for taking action. 6. Pressure-Test Your Action: But what if the action you choose to take on a trend is the wrong one? In this final step, you must make sure the strategy you take on a trend will deliver the desired outcome, and that requires asking difficult questions about both the present and the future.

The logical next step after determining where a trend is on its trajectory is to follow with an “if this, then that” statement. Step five of our forecasting method is to use Kahn’s technique of storytelling and put the facts into a narrative context to develop possible scenarios for the future. Our goal isn’t to predict something that will definitely happen. Instead, we must envision all of the possible outcomes and use them to help us make an informed decision about strategy to employ in the present.

Trends are more important now. It’s harder to reach everyone at one time, so you need to understand trends within individual groups or tribes. And with the rise in word of mouth, trends can give you data on the right places to start having a dialogue with consumers. Liisa Puolakka, Head of Brand Identity, Nokia