Posts Tagged ‘forecast error’
Practical Time Series Forecasting – Meta Models
“There are two kinds of forecasters: those who don’t know, and those who don’t know they don’t know.” ― John Kenneth Galbraith After an extensive model building and vetting process, along the lines we previously discussed here and here, the practical forecaster may still be left with several strong performing models. These models perform similarly…
Read MorePractical Time Series Forecasting – Know When to Roll ‘em
“Prediction is very difficult, especially if it’s about the future.” ― Niels Bohr, physicist Holdout samples are a key component to estimating a “useful” forecasting model. Set aside data at least equal in length to your forecast horizon (“holdout sample”). Build your models on the remaining data (“modeling sample”). And compare the candidate models’ forecast…
Read MorePractical Time Series Forecasting – To Difference or Not to Difference
“It is sometimes very difficult to decide whether trend is best modeled as deterministic or stochastic, and the decision is an important part of the science – and art – of building forecasting models.” ― Diebold, Elements of Forecasting, 1998 A time series can have a very strong trend. Visually, we often can see it. Gross…
Read MorePractical Time Series Forecasting – Know When to Hold ‘em
“The only relevant test of the validity of a hypothesis is comparison of prediction with experience.” ― Milton Friedman, economist Holdout samples are a mainstay of predictive analytics. Set aside a portion of your data (say, 30%). Build your candidate models. Then “internally validate” your models using the holdout sample. More sophisticated methods like cross…
Read MorePractical Time Series Forecasting – To Difference or Not to Difference
“It is sometimes very difficult to decide whether trend is best modeled as deterministic or stochastic, and the decision is an important part of the science – and art – of building forecasting models.” ― Diebold, Elements of Forecasting, 1998 A times series can have a very strong trend. Visually, we often can see it.…
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