How deeply does Demand Forecasting Best Practices cover feature engineering?

Defines selection, creation, and modification, then gives brainstorming prompts but defers model-parameter tuning.

Feature engineering

As explained in Demand Forecasting Best Practices

Feature engineering is the selection, creation, or modification of input features for a machine learning model.

Feature engineering selects, creates, or tweaks the data features supplied to a machine-learning model. For demand forecasting, business knowledge helps identify information the model would otherwise miss, such as whether a product will be on sale during Black Friday. section 15.1.1

Begin with the decision question: if forecasting next month’s demand, what would you ask or check? Brainstorm with people who understand pricing, promotions, inventory, and demand before collecting the required data. section 15.1.1

Useful brainstorming prompts include:

What is the current price, and has it changed recently? section 15.1.1
What is the product’s average monthly demand? section 15.1.1
Was the product recently out of stock, or is a promotion running? section 15.1.1

Table 15.2 Typical Inputs (or Data Features) for a Machine-Learning Model (view table figure)

Inputs								Outputs
Brand	Demand				Price		Promotion	Demand
Brand	Q–4	Q–3	Q–2	Q–1	Average	Q+1	Q+1	Q+1
Low cost	1,500	500	400	300	10	10		200
Premium	500	1,000	750	500	35	30	–15%	700
Regular	250	350	150	400	20	20	–20%	300
Low cost	100	110	120	150	8	9		120
Low cost	50	30	80	10	5	5		30
Regular	200	250	220	240	15	14	–10%	260

Compare typical model inputs—including brand, price, promotion, and historical demand—with predicted demand. section 15.1.1

How Demand Forecasting Best Practices covers it

Cover of Demand Forecasting Best Practices

liveBook manning.com

Read Demand Forecasting Best Practices on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIFeature processing is the transformation or elaboration of input features to make them suitable or more informative for machine learning models.
liveBook manning.com

Deep Learning with R, Third EditionFeature engineering is the manual design or transformation of input features into representations suitable for machine learning.
liveBook manning.com

Deep Learning with Python, Third EditionThe manual design or transformation of input variables into representations better suited for a learning algorithm.
liveBook manning.com

Knowledge Graphs and LLMs in ActionManual feature extraction is the hand-designed selection or computation of features from raw data.
liveBook manning.com

Natural Language Processing in Action, Second EditionFeature engineering is the deliberate construction or transformation of input representations to make them useful for predictive modeling.
liveBook manning.com

Outlier Detection in PythonFeature engineering is the process of modifying or combining original data attributes to create inputs that are more useful for analysis or modeling.
liveBook manning.com

Machine Learning System DesignFeature engineering is the process of constructing, transforming, or selecting input features for a machine learning model.
liveBook manning.com

Effective Data AnalysisFeature engineering is the creation or transformation of variables from existing data to better represent processes relevant to a model.
liveBook manning.com

Financial AI in PracticeFeature engineering is the process of constructing informative input variables from raw data for statistical or machine-learning models.
liveBook manning.com

Delivering Machine Learning Projects: From design to deploymentFeature engineering is the transformation of raw data into informative input variables that represent useful structure for a machine learning model.
liveBook manning.com

Sutskever's ListFeature engineering is the process of designing input representations from raw data for use by a learning algorithm.
liveBook manning.com

The Art of AI Product DevelopmentFeature engineering is the process of creating, selecting, transforming, and refining input features to improve a machine-learning model's results.
liveBook manning.com

Distributed Machine Learning PatternsFeature engineering is the process of creating, transforming, or selecting input features to improve a machine learning model.
liveBook manning.com

Digital Twins in ActionFeature engineering is the process of constructing informative input variables from raw data for analysis or machine learning.
liveBook manning.com

Evolutionary Deep Learning: Genetic algorithms and neural networksFeature engineering is the construction, transformation, or selection of input variables that represent useful information for a machine-learning model.

Compare how 16 books explain feature engineering →

Related concepts

foundational modeluses · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.
feature selectionhas part · Feature selection is the process of choosing a subset of input features for use in a modeling, analysis, or prediction task.
data layerpart of · A data layer is an architectural part of a system that organizes access to, management of, or persistence of data for other components.
pipelinepart of · A pipeline is an ordered or coordinated sequence of connected processing stages or tasks that passes data, work, or results toward an outcome.
language modeladdressed by · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
signalproduces · A signal is an observable value, event, or pattern that conveys information about a condition, state, or phenomenon.

This explainer was generated by Manning's knowledge graph from the text of Demand Forecasting Best Practices. The section links point to the parts of the book it draws on.