3.3 Time Series Use Case

You work in an electronics store, and sales of laptops and tablets have increased over the past two quarters. You want to forecast product sales for the next four quarters using historical timestamped data. To do this, you apply the Exponential Smoothing algorithm, which predicts future sales by analyzing patterns over evenly spaced time intervals in the historical data.

Table 3-1 Related Content

Topic Link
About Time Series About Time Series
About Model Setting About Model Setting
Shared Settings Shared Settings
Time Series Algorithm Time Series Algorithm

Before you start your OML4R use case journey, ensure that you have the following:

  • Data Set

    The data set used for this use case is from the SH schema. The SH schema can be readily accessed in Oracle Autonomous Database. For on-premises databases, the schema is installed during the installation or can be manually installed by downloading the scripts. See Installing the Sample Schemas.

    You will use the SALES table from the SH schema. You can access the table by running the SELECT statements in OML Notebooks.

  • Database

    Select or create a database using one of the following options:

  • Machine Learning Tools

    Use OML Notebooks for Oracle Autonomous Database.

Topics:

  • Access Data
    Access the data set from the SH Schema and explore the data to understand the attributes.
  • Explore Data
    Explore the data to understand its structure and assess its quality. At this stage, identify the data types and detect any noise present. Check for missing values as well as numeric outliers.
  • Build Model
    To build a model using time series data, apply the Exponential Smoothing algorithm to the proxy object ESM_SH_DATA created during the exploratory stage.
  • Evaluate
    Evaluate your model by reviewing diagnostic metrics and performing quality checks.

Related Topics

Parent topic: Use Cases