Forecast Analytics, a component of Lumenore’s Advanced Analytics suite titled “Do You Know,” serves as a pivotal domain within Data Science, leveraging machine learning techniques to determine the maximum likelihood of an event. Predictive analysis within this module extrapolates and anticipates future values by harnessing historical data sourced both internally and externally across the enterprise.

This analytics segment delves into historical datasets, extracting valuable trends and patterns through statistical analysis. It formulates strategies based on these insights to effectively process new data, predicting the most optimal outcomes.

Consider scenarios like forecasting the volume of calls anticipated for the upcoming month or quarter in a call center or predicting the average handling duration for call agents supervised by XYZ. Before initiating the configuration for predictive analysis, it’s essential to contextualize the specific Key Performance Indicator (KPI) and seamlessly complete the remaining steps in the process.

How to Perform Forecast Analysis in Lumenore

Step 1: After accessing “Do You Know”, select “Forecast”.

Step 2: Click on “Create New Insight Forecast”.

Step 3: Choose a Schema and click “Next”.

Note: Ensure prerequisites (A KPI, Date, and Attribute) for forecast analysis are met.

Step 4: Select the following:

• Select forecast metric: Select a singular metric to conduct predictive analysis.
• Select input measure: Choose input metric for predicting the output metric.
• Select the forecast date attribute: To forecast your metric, data needs to be serialized and aggregated over time.
• Selecting the Algorithm for the Forecast: Various time series forecasting algorithms are available, each with distinct strengths. Lumenore provides the following algorithms:

Regression:

• Used for predicting future values based on past observations.
• In Lumenore, the optimal window size is automatically selected, simplifying the forecasting process.

Exponential Smoothing:

• Emphasizes recent observations for short-term forecasts.
• Efficient and applicable to a wide range of time series data.

SARIMA (Seasonal-ARIMA):

• Models seasonality in the series.
• ARIMA (Auto-Regressive Integrated Moving Average) with seasonal parameters.

Theta Method:

• Extracts linear trend and curvature.
• Simple yet effective, especially for time series practitioners.

Prophet:

• Fits non-linear trends with yearly, weekly, and daily seasonality.
• Suitable for time series with strong seasonal effects.
• Select forecast time range: Duration into the future for which forecasts are generated based on historical data and predictive models.
• Selecting Split Ratio (Train: Test): Determines the proportion of data used for training and testing the model. Commonly used ratios include 70:30 or 80:20, preventing overfitting and ensuring the model generalizes well.
• Remove outliers: Remove outliers from the selected KPIs.
• Do you want to add filters?: Optionally add filters.

Note: If you wish to apply filter a window for creating filters appears. Establish filters by groups or conditions as needed.

Step 5: Customize insights narration and click “Save”.

Step 6: Name the insight for future reference and click “Save/Update.”

Step 7: A new window appears; click “Execute Now” to generate insights.

Output (Insights)

For clarity, an insight for forecast analysis has been created using profit (forecast metric), order date-quarterly (forecast date attribute), theta (algorithm), and Train 80% Test 20% (Split ratio). The resulting forecast for profit is depicted in the chart, where the green curve represents the predicted values.