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Forecasting: principles and practice. Welcome to our online textbook on forecasting.

Forecasting: principles and practice

This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will fill in many of those details. The book is written for three audiences: (1) people finding themselves doing forecasting in business when they may not have had any formal training in the area; (2) undergraduate students studying business; (3) MBA students doing a forecasting elective.

We use it ourselves for a second-year subject for students undertaking a Bachelor of Commerce degree at Monash University, Australia. For most sections, we only assume that readers are familiar with algebra, and high school mathematics should be sufficient background. 13 Resources. Time series - AR(1) selection using sample ACF-PACF. Forecasting within limits. Forecasting within limits It is com­mon to want fore­casts to be pos­i­tive, or to require them to be within some spec­i­fied range .

Forecasting within limits

Both of these sit­u­a­tions are rel­a­tively easy to han­dle using transformations. Pos­i­tive forecasts To impose a pos­i­tiv­ity con­straint, sim­ply work on the log scale. . Interpreting noise. When watch­ing the TV news, or read­ing news­pa­per com­men­tary, I am fre­quently amazed at the attempts peo­ple make to inter­pret ran­dom noise.

Interpreting noise

For exam­ple, the lat­est tiny fluc­tu­a­tion in the share price of a major com­pany is attrib­uted to the CEO being ill. When the exchange rate goes up, the TV finance com­men­ta­tor con­fi­dently announces that it is a reac­tion to Chi­nese build­ing con­tracts. No one ever says “The unem­ploy­ment rate has dropped by 0.1% for no appar­ent reason.” What is going on here is that the com­men­ta­tors are assum­ing we live in a noise-​​free world. They imag­ine that every­thing is explic­a­ble, you just have to find the expla­na­tion. The finance news Every night on the nightly TV news bul­letins, a sup­posed expert will go through the changes in share prices, stock prices indexes, cur­rency rates, and eco­nomic indi­ca­tors, from the past 24 hours.

Errors on percentage errors. The MAPE (mean absolute per­cent­age error) is a pop­u­lar mea­sure for fore­cast accu­racy and is defined as where denotes an obser­va­tion and denotes its fore­cast, and the mean is taken over Arm­strong (1985, p.348) was the first (to my knowl­edge) to point out the asym­me­try of the MAPE say­ing that “it has a bias favor­ing esti­mates that are below the actual val­ues”.

Errors on percentage errors

Modelling seasonal data with GAMs. In previous posts I have looked at how generalized additive models (GAMs) can be used to model non-linear trends in time series data.

Modelling seasonal data with GAMs

At the time a number of readers commented that they were interested in modelling data that had more than just a trend component; how do you model data collected throughout the year over many years with a GAM? In this post I will show one way that I have found particularly useful in my research. First an equation. Monthly seasonality. I regularly get asked why I don’t consider monthly seasonality in my models for daily or sub-daily time series.

Monthly seasonality

For example, this recent comment on my post on seasonal periods, or this comment on my post on daily data. The fact is, I’ve never seen a time series with monthly seasonality, although that does not mean it does not exist. Monthly seasonality would occur if there is some regular activity that takes place every month and which affects the time series. For example, some companies try to average their expenditure across the month and often have to spend more at the end of the month to justify the budget.

So daily expenditure tends to increase at the end of each month, producing a monthly seasonal pattern. Or imagine a situation where a company always stocks up on supplies on the second Tuesday in every month. This type of seasonal pattern is quite difficult to model. Rmnppt/FruitAndVeg - HTML - GitHub. Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance. Why time series forecasts prediction intervals aren't as good as we'd hope. Five different sources of error When it comes to time series forecasts from a statistical model we have five sources of error: Random individual errors Random estimates of parameters (eg the coefficients for each autoregressive term) Uncertain meta-parameters (eg number of autoregressive terms) Unsure if the model was right for the historical data Even given #4, unsure if the model will continue to be right A confidence interval is an estimate of the statistical uncertainty of the estimated parameters in the model.

Why time series forecasts prediction intervals aren't as good as we'd hope

It usually estimates the uncertainty source #2 above, not interested in #1 and conditional on the uncertainty of sources #3, #4 and #5 all being taken out of the picture. A prediction interval should ideally take all five sources into account (see Rob Hyndman for more on the distinction between prediction and confidence intervals). Here’s a simple simulation to show the cost of estimating the meta-parameters, even when sources of error #4 and #5 can be discounted.

Code. Prophet.