Big Data/Analytics Zone is brought to you in partnership with:

Rob Hyndman is a Professor of Statistics at Monash University, Australia. He is Editor-in-Chief of the International Journal of Forecasting and author of over 100 research papers in statistical science. He also maintains an active consulting practice, assisting hundreds of companies and organizations. His recent consulting work has involved forecasting electricity demand, tourism demand, the Australian government health budget and case volume at a US call centre. Rob J is a DZone MVB and is not an employee of DZone and has posted 47 posts at DZone. You can read more from them at their website. View Full User Profile

Why Are Some Things Easier to Forecast Than Others?

09.19.2012
| 3087 views |
  • submit to reddit

Fore­cast­ers are often met with skep­ti­cism. Almost every time I tell some­one that I work in fore­cast­ing, they say some­thing about fore­cast­ing the stock mar­ket, or fore­cast­ing the weather, usu­ally sug­gest­ing that such fore­casts are hope­lessly inac­cu­rate. In fact, fore­casts of the weather are amaz­ingly accu­rate given the com­plex­ity of the sys­tem, while any­one claim­ing to fore­cast the stock mar­ket deserves skep­ti­cism. So what is the dif­fer­ence between these two types of fore­casts, and can we say any­thing about what can be rea­son­ably be fore­cast and what can’t?

Clearly, some things are eas­ier to fore­cast than oth­ers. The time of the sun­rise tomor­row morn­ing can be fore­cast very pre­cisely. On the other hand, tomorrow’s lotto num­bers can­not be fore­cast with any accu­racy. The pre­dictabil­ity of an event or a quan­tity depends on sev­eral fac­tors including:

  1. how well we under­stand the fac­tors that con­tribute to it;
  2. how much data are available;
  3. whether the fore­casts can affect the thing we are try­ing to forecast.

For exam­ple, fore­casts of elec­tric­ity demand can be highly accu­rate because all three con­di­tions are usu­ally sat­is­fied. We have a good idea on the con­tribut­ing fac­tors:  elec­tric­ity demand is largely dri­ven by tem­per­a­tures, with smaller effects for cal­en­dar vari­a­tion such as hol­i­days, and eco­nomic con­di­tions. Pro­vided there is a suf­fi­cient his­tory of data on elec­tric­ity demand and weather con­di­tions, and we have the skills to develop a good model link­ing elec­tric­ity demand and the key dri­ver vari­ables, the fore­casts can be remark­ably accurate.

On the other hand, when fore­cast­ing cur­rency exchange rates, only one of the con­di­tions is sat­is­fied: there is plenty of avail­able data. How­ever, we have a very lim­ited under­stand­ing of the fac­tors that affect exchange rates, and the fore­casts of the exchange rate have a direct effect on the rates them­selves. If there are well-​​publicized fore­casts that the exchange rate will increase, then peo­ple will imme­di­ately adjust the price they are will­ing to pay and so the fore­casts are self-​​fulfilling. In a sense the exchange rates become their own fore­casts. This is an exam­ple of the effi­cient mar­ket hypoth­e­sis. Con­se­quently, fore­cast­ing whether the exchange rate will rise or fall tomor­row is about as pre­dictable as fore­cast­ing whether a tossed coin will come down as a head or a tail. In both sit­u­a­tions, you will be cor­rect about 50% of the time what­ever you fore­cast. In sit­u­a­tions like this, fore­cast­ers need to be aware of their own lim­i­ta­tions, and not claim more than is possible.

Often in fore­cast­ing, a key step is know­ing when some­thing can be fore­cast accu­rately, and when fore­casts are no bet­ter than toss­ing a coin. Good fore­casts cap­ture the gen­uine pat­terns and rela­tion­ships which exist in the his­tor­i­cal data, but do not repli­cate past events that will not occur again.

Many peo­ple wrongly assume that fore­casts are not pos­si­ble in a chang­ing envi­ron­ment. Every envi­ron­ment is chang­ing, and a good fore­cast­ing model cap­tures the way things are chang­ing. Fore­casts rarely assume that the envi­ron­ment is unchang­ing. What is nor­mally assumed is that the way the envi­ron­ment is chang­ing will con­tinue into the future. That is, that a highly volatile envi­ron­ment will con­tinue to be highly volatile; a busi­ness with fluc­tu­at­ing sales will con­tinue to have fluc­tu­at­ing sales; and an econ­omy that has gone through booms and busts will con­tinue to go through booms and busts. A fore­cast­ing model is intended to cap­ture the way things move, not just where things are. As Abra­ham Lin­coln said “If we could first know where we are and whither we are tend­ing, we could bet­ter judge what to do and how to do it.”

Published at DZone with permission of Rob J Hyndman, author and DZone MVB. (source)

(Note: Opinions expressed in this article and its replies are the opinions of their respective authors and not those of DZone, Inc.)

Comments

Taruvai Subramaniam replied on Thu, 2012/09/20 - 9:35am

Comment viewing options

Select your preferred way to display the comments and click "Save settings" to activate your changes.