3.4 Time Series Use Case

You work in an electronic store, and sales of laptops and tablets have increased over the last two quarters. You want to forecast your product sales for the next four quarters using historical timestamped data. You forecast sales using the Exponential Smoothing algorithm, predicting changes over evenly spaced intervals of time using historical data.

Table 3-2 Related Content

Topic Link
OML4Py GitHub Example Time Series - Exponential Smoothing
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 OML4Py 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 and assess the quality of the data. At this stage assess the data to identify data types and noise in the data. Look for missing values and numeric outlier values.
  • Build Model
    To build a model using the time series data, use the Exponential Smoothing algorithm on the OML proxy object ESM_SH_DATA generated during the exploratory stage.
  • Evaluate
    Evaluate your model by viewing diagnostic metrics and performing quality checks.

Related Topics

Parent topic: Use Cases