Time Series Feature Engineering¶
Objective¶
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¶
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.
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
Seven step moving average
- For seven step, window size will be from -7 to 7 for sales data
- Moving average output
Extract Date Time Features¶
- Break date and get the year, month, week of year, day of the month, hour, minute, second, etc.
- Output of Date Time Features
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
Lag seven
- Window shift is seven
