Posts Tagged ‘regression’
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 – Data Science Taxonomy
“Big data is not about the data.*” ― Gary King, Harvard University (*It’s about the analytics.) Machine Learning. Deep Learning. Data Science. Artificial Intelligence. Big Data. Not a day goes by that one or all of these buzzwords stream past in our business news feeds. Data analytics has become mainstream. And you better jump on…
Read MorePractical Time Series Forecasting – Potentially Useful Models
“All models are wrong, but some are useful.” ― attributed to statistician George Box This quote pretty well sums up time series forecasting models. Any given model is unlikely to be spot on. And some can be wildly off. But through a careful methodical process, we can whittle the pool of candidate models down to…
Read MorePractical Time Series Forecasting – Some Basics
“The long run is a misleading guide to current affairs. In the long run we are all dead.” ― John Maynard Keynes, A Tract on Monetary Reform Forecasting the future is an exercise in uncertainty. And the further out one looks, the more uncertain the forecast becomes. Most businesses are keenly focused on the next…
Read MorePractical Time Series Forecasting – Introduction
“The only thing I cannot predict is the future.” ― Amit Trivedi, Riding The Roller Coaster: Lessons from financial market cycles we repeatedly forget It goes without saying that every business is keenly interested in knowing what the future will bring. Will sales grow next year? By how much? Will suppliers increase their prices? How…
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