Time Series Feature Engineering¶
Fire Insights provides a number of Processors for Feature Engineering of Time Series Data. These include:
| Features | Description |
|---|---|
| DateTimeFieldExtract | Extracts year, month, day of month, hour, minute, second and week of year from timestamp/date columns |
| Days to holiday | Days remaining for next holiday |
| Days from holiday | Days passed after holiday |
| Time-segmentation | Divide data in morning, afternoon, evening, night to get more idea about time based pattern |
| MovingWindowingFunctions | Calculates the moving values using the given function |
| WindowingAnalytics | Implements window functions is mainly through the operators rolling and expanding |
| Exponential Moving Average (EMA) | The Exponential Moving Average (EMA) assigns a greater weight to the most recent price observations. While it assigns lesser weight to past data, it is based on a recursive formula that includes in its calculation all the past data in our price series. |
DateTimeFieldExtract¶
Below is the sample workflows which contains DateTimeFieldExtract processor in Fire Insights.
It reads the JetRail Train dataset & use DateTimeFieldExtract processor which create New DataFrame by extracting Date & Time field and print the result.
DateTimeFieldExtract processor Configuration:
Output result of DateTimeFieldExtract processor:
MovingWindowingFunctions¶
Below is the sample workflows which contains MovingWindowingFunctions processor in Fire Insights.
It reads the ticker dataset, concatenate the input column, casting specified column to new data type, use MovingWindowingFunctions processor which calculates the moving value of selected function of input column and print the result.
MovingWindowingFunctions processor Configuration:
Output result of MovingWindowingFunctions processor:
