Time Series Feature Engineering


It is a process of extracting new features from raw data via data mining techniques. These features can be used to improve the performance of models.


Dataset contains 4 columns as below:

  • Date - Date when product was sold
  • Store - Store id from where product got sold
  • Item - Item id
  • Sales - Quantity of product sold

Create new feature from existing table to improve performance of models

Feature Engineering Workflow

Each column is a feature. But all features may not produce the best results from models, so feature engineering plays an important role in choosing the right features. A model will not entirely improve its prescient force, yet will offer the adaptability to utilize less unpredictable models that are quicker to run and more handily.

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Moving average

One step moving average

  • Moving average is commonly used to streamline short-period fluctuations in time series data and feature long-term patterns.
  • For one step, window size will be from -1 to 1 for sales data
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Seven step moving average

  • For seven step, window size will be from -7 to 7 for sales data
  • Moving average output
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Extract Date Time Features

  • Break date and get the year, month, week of year, day of the month, hour, minute, second, etc.
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  • Output of Date Time Features
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Lags Feature

  • Lag is used to make non-stationary data into stationary data
  • Outliers are easily discernible on a lag plot
  • acf and pacf plot is used to calcluate best lags

Lag one

  • The most commonly used lag is 1, called a first-order lag
  • Window shift is one
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Lag seven

  • Window shift is seven
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New feature data

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