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Printable Handouts
Navigable Slide Index
- Introduction
- Types of forecasting methodologies
- The forecasting methods spectrum
- Statistics: Valid role, but not the answer!
- Where judgemental methods are most appropriate
- Commonly used judgemental methods
- Where time series methods are most appropriate
- Commonly used time series methods
- Where causal methods are most appropriate
- Commonly used causal methods
- Where machine learning methods are most appropriate
- Commonly used machine learning methods
- Where clustering methods are most appropriate
- Commonly used clustering methods
- Balancing statistics with other important considerations
This material is restricted to subscribers.
Topics Covered
- Judgmental methods
- Time series methods
- Causal methods
- Machine-Learning methods
- Clustering methods
Links
Series:
Categories:
Talk Citation
Wilson, E. (2023, May 31). General types of forecasting methods [Video file]. In The Business & Management Collection, Henry Stewart Talks. Retrieved October 12, 2024, from https://doi.org/10.69645/LKAP3179.Export Citation (RIS)
Publication History
Other Talks in the Series: Business Forecasting and Projections
Transcript
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0:00
Hello, my name is Eric Wilson.
I'm the Director of
Thought Leadership with
the Institute of
Business Forecasting.
Today I want to talk
about general types
of forecasting methods.
0:12
When you're looking
at forecasting
method there is a lot of
different methodologies
available to
you that can build a lot
of different models.
Institute of Business
Forecasting,
IBF does a lot of research
around what types of
models people are using.
Right now, about 50% of the
models out there utilized by
most practitioners in businesses
are time series type of models.
Which makes sense because
a lot of the data that's
available to organizations is
with inside their
own four walls.
It's going to be
shipment history,
it's going to be order history.
It's going to be things
that are time related that
inside their own four walls that
they then can forecast with.
That doesn't mean there's not
other forecasting methods
and models available.
There's a lot of other types of
methodologies available as well.
Causal models right now
make up about 17% or so
of the methods that most
companies and people are using.
This looks at
external variables or
other types of relationship
type variables and
tries to find those patterns
and be able to
forecast with those.
What we've seen recently
with the more data
available with more
processing power available,
with new types of methods
that are now accessible to
practitioners we're
seeing new types
of methodologies coming
available as well.
These are more of your AI or
machine learning types of
models and they actually
make up approximately
about 15% of
the methods that companies
are currently
utilizing right now.
There is one final type of
method I do want to mention,
and that's the
judgmental models.
Those are still relevant
and important today,
and I don't see them
going away at any point.
Judgment is still part of
the forecasting process.
It's still something
we need to bring into
a process to build
better forecast,
and there's judgemental type of
models that you can utilize
to bring in methods,
different types of inputs,
and be able to improve
your forecasts as well.