You can configure the properties of the HANA Demand Forecasting component.
Prerequisites:
Configuration Process:
When configuring the HANA Demand Forecasting
component, it is mandatory to map information about schema, table and column names from
your HANA sales table. You perform the mapping in the Variables section of the
component's Properties tabbed page.
You have the option to configure the settings in
the remaining sections of the Properties tabbed page, and the configurations
settings in the Advanced and General tabbed pages.
To configure the component,
take the following steps:
- In Expert Analytics, connect to a HANA Data Source. This data source
is your sales table.
- Navigate to the Predict Room.
- In the Predict Room, from the Component List select Time
Series - HANA Demand Forecasting.
Drag-and-drop the HANA Demand Forecasting component to
the analysis editor. Alternatively, double-click the HANA Demand
Forecasting component. Click OK.
- To open the configuration settings, double-click the HANA Demand
Forecasting component. Alternatively, on the component click the
Settings icon
and, from the context menu,
select Configure Settings.
- In the Properties panel of the HANA Demand Forecasting dialog box, the Forecast
Horizon section enables you to set the prediction period. Set the
Start and End dates.
- In the Variables section, you map information from your sales table to the
component. Configure the following settings:
- Product ID: Select the string-only column from
the input table that contains the product identifier code, which can be
up to 60 characters long.
- Location ID: Select the string-only column from
the input table that contains the location identifier code, which can be
up to 60 characters long.
- Transaction Timestamp. Select the column from
the input table that contains the transaction timestamp, which must be
in date or timestamp format.
- Unit Sales. Select the numeric-only column from
the input table that contains the unit sales figure.
- Revenue: Select the numeric-only column from the
input table that contains the revenue figure.
- Optionally, in the Holidays section, configure the following settings:
- Schema: Select the schema for the input table.
- Tables: Select a table from the schema.
- Views: Select a view from the schema.
- Time Stream ID: Select the string-only column from the input table
that contains the time stream identifier code, which can be up to 10
characters long.
- Public Holiday Key: Select the string-only column from the input
table that contains the public holiday key, which can be up to 3
characters long.
- Operational Status: Select the integer-only column from the input
table that contains the operational status.
- Timestamp: Select the column from the input table that contains
the transaction timestamp, which must be in date or timestamp
format.
- Optionally, in the Locations to Holiday Mapping section, configure the following settings:
- Schema: Select the schema that contains the table with information
about mapping locations to public holidays.
- Tables: Select a table from the schema.
- Views: Select a view from the schema.
- Location ID: Select the string-only column from the input table
that contains the location identifier code, which can be up to 60
characters long.
- Holiday ID: Select the string-only column from the input table
that contains the holiday identifier code, which can be up to 10
characters long.
- Optionally, in the Demand Influencing Factors section, configure the following settings:
- Schema: Select the schema that contains the table with information
about Demand Influencing Factor..
- Tables: Select a table from the schema.
- Views: Select a view from the schema.
- Product ID: Select the string-only column from the input table
that contains the product identifier code.
- Location ID: Select the string-only column from the input table
that contains the location identifier code.
- DIF Tag: Select the string-only column from the input table that
contains the demand influencing factor tag, which can be up to 32
characters long.
- DIF Attribute: Select the string-only column from the input table
that contains the demand influencing factor attribute, which can be up
to 32 characters long.
- Timestamp From: Select the date-only column from the input table
that contains the date that the timestamp begins.
- Timestamp To: Select the date-only column from the input table
that contains the date that the timestamp ends.
- DIF Value: Select the numeric-only column from the input table
that contains the Demand Influencing Factor (DIF) value.
- Optionally, in the Expected Future Prices section, configure the following settings:
- Schema: Select the schema that contains the table with information
about Expected Future Prices.
- Tables: Select a table from the schema.
- Views: Select a view from the schema.
- Product ID: Select the string-only column from
the input table that contains the product identifier code.
- Location ID: Select the string-only column from
the input table that contains the location identifier code.
- Timestamp From: Select the date-only column from
the input table that contains the date that the timestamp begins.
- Price: Select the numeric-only column from the
input table that contains the price.
- Optionally, navigate to the Advanced tabbed page. In the Configuration Parameters section,
configure the following settings:
- Damping factor (FC_TREND_DAMP): Defines the damping factor for the
trend regressor. The range is value >= 0.00000.
- Total regress mass (MOD_HDM_NEAR_HOLIDAY_DENSITY): Sets the
proportion of total regressor mass to the right (POST: left) of the
half-way date. Note that there are two groups of HDM-regressors,
SYS:CAL:YR:HDM:PRE:* and SYS:CAL:YR:HDM:POST:*. The PRE-regressors
define the ramp-up before the holiday, the POST-regressors the ramp-down
after the holiday. The range is 0.50000 <=value < 1.00000.
- Optionally, in the Time Delay Effect section, configure the following settings:
- Observation weight (MOD_TIME_WEIGHT): Sets the
weight of a one-year-old observation in modelling, compared to an
observation taken today. This way, the variable helps to decide whether
to give equal importance (or weight) to all the records irrespective of
their timestamps. For example, when building the model, setting the
parameter value to 1 gives equal importance to all the records
irrespective of the time that they were recorded. Whereas, setting a
value to the parameter less than 1 enables the user to give less
importance to the records that have older timestamps, as compared to the
records that have recent timestamps. The range is 0.50000 <=value
< 1.00000.
- Lower Boundary on weight (MOD_TIME_WEIGHT_MIN):
Defines a lower boundary below which the weight will not fall. The range
is 0.00001 <=value <= 1.00000.
- Optionally, in the Out of Stock Detection section, configure the following settings:
- Zero sales period(MOD_OOSD_MIN_LEN): Sets the minimum length of
the continuous zero sales to be considered for an out-of-stock period
evaluation. The range is value >= 1.00000.
- Probability threshold (MOD_OOSD_THRSHLD): Sets the threshold for
the probability score to determine whether an item is out-of-stock. The
probability score for each item is derived based on the occurrence of
zero sales for a period greater than the values specified in the
parameter, MOD_OOSD_MIN_LEN. The range is value >= 1.00000.
- Optionally, select the appropriate Time Series Decomposition checkboxes to decompose and
clearly see the influence on your results of Seasonality,
User Promotion and Holidays.
De-select the checkboxes if you do not want to consider the impact of these
factors in your results.
- Optionally, in the Outlier Detection section, configure the following settings:
- Configure outlier detection: Defines whether to
switch on or off outlier detection.
- Acceptable distance from mean
(MOD_OUTLIER_MEAN_FACTOR): Defines the outlier detection factor to
determine how far away from the mean is acceptable.
- Min. non-zero observations (MOD_OUTLIER_STD_DEV_FACTOR): Defines
the outlier detection minimum number of non-zero observations, regular
and promotional, counted before 0-filling.
- Standard deviation
factor(MOD_OUTLIER_STD_DEV_FACTOR): Defines the outlier
detection factor to determine how many deviations away from the mean is
acceptable.
- When you have configured the necessary settings, click Done.
- Optionally, in the General tabbed page, set properties in the Basic section,
such as a component Alias Name and
Description.
- Click the Run Analysis
icon.
- When the analysis executes, click OK on the notification message.
- Click the Results tab to view the results.
View the Results Grid:
In the Results tabbed page, the results grid is shown by
default. For a description of each column in the grid, see Results
Grid.
View the Algorithm Summary:
Click Summary to see an overview from the algorithm that
describes the behavior of the product location combinations in relation to price
elasticity. For a detailed description, see Algorithm
Summary.
View the Graph
Click Model Representation to see a graph that displays data
points for both the historical data and the forecast range. You can zoom-in on the
graph to isolate any portion. For example, to zoom-in on the predicated values for
the Forecasted Sales (which are graphed in yellow lines at the end of the graph),
select the final portion of the Slider control bar under the graph. The graph will
change focus to display the Forecasted Sales.
Export the Analysis:
You can export a Demand Forecast analysis as a stored procedure. For a detailed
description, see Exporting an
Analysis as a Stored Procedure.
You can now configure HANA Demand Forecasting component to forecast future
sales.