1. Machine Learning for SQL Use Cases
  2. Concepts
  3. Machine Learning Algorithms
  4. About Exponential Smoothing
  5. Data Preparation for Exponential Smoothing Models
  6. Input Data

Input Data

Time series analysis requires ordered input data. Hence, each data row must consist of an [index, value] pair, where the index specifies the ordering.

When you create an Exponential Smoothing (ESM) model using the CREATE_MODEL or the CREATE_MODEL2 procedure, the CASE_ID_COLUMN_NAME and the TARGET_COLUMN_NAME parameters are used to specify the columns used to compute the input indices and the observed time series values, respectively. The time column bears Oracle number, or Oracle date, timestamp, timestamp with time zone, or timestamp with local time zone. When the case id column is of type Oracle NUMBER, the model considers the input time series to be equally spaced. Only the ordinal position matters, with a lower number indicating a later time. In particular, the input time series is sorted based on the value of case_id (time label). The case_id column cannot contain missing values. To indicate a gap, the value column can contain missing values as NULL. The magnitude of the difference between adjacent time labels is irrelevant and is not used to calculate the spacing or gap size. Integer numbers passed as CASE_ID are assumed to be non-negative.

ESM also supports partitioned models and in such cases, the input table contains an extra column specifying the partition. All [index, value] pairs with the same partition ID form one complete time series. The Exponential Smoothing algorithm constructs models for each partition independently, although all models use the same model settings.

Data properties may result in a warning notice, or settings may be disregarded. If the user sets a model with a multiplicative trend, multiplicative seasonality, or both, and the data contains values Yt<= 0, the model type is set to default. If the series contains fewer values than the number of seasons given by the user, then the seasonality specifications are ignored and a warning is issued.

If the user has selected a list of predictor series using the parameter EXSM_SERIES_LIST, the input data can also include up to twenty additional time series columns.

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