MSDN Documentation

Time Series Algorithm

The Time Series algorithm in SQL Server Analysis Services (SSAS) is designed for forecasting future values based on historical data patterns. It's particularly useful for predicting trends, seasonal fluctuations, and identifying cyclical behavior in time-dependent datasets.

Overview

This algorithm leverages statistical models to analyze time series data. It identifies patterns such as trends, seasonality, and cycles to generate predictions. The algorithm supports multiple forecasting models, including:

• ARIMA (AutoRegressive Integrated Moving Average)
• ARTMAP (Adaptive Resonance Theory Map)
• Linear Regression
• Linear Trend

Key Concepts

• Time Series Data: A sequence of data points indexed in time order.
• Trend: The general direction of the data over a long period.
• Seasonality: Predictable patterns that repeat over a fixed period (e.g., daily, weekly, yearly).
• Cycle: Fluctuations that are not of a fixed period, often associated with economic or business cycles.
• Forecasting: The process of predicting future values based on past and present data.

Data Requirements

To use the Time Series algorithm effectively, your data should have the following characteristics:

• A column representing the time sequence (e.g., dates, timestamps).
• A predictable time interval between data points.
• Sufficient historical data to identify patterns.

Algorithm Parameters

The Time Series algorithm offers several parameters to customize its behavior and improve forecast accuracy. Key parameters include:

Using the Algorithm

You can implement the Time Series algorithm in SSAS using:

• SQL Server Data Tools (SSDT) for Visual Studio.
• AMO (Analysis Management Objects) for programmatic control.
• DMX (Data Mining Extensions) queries.

Example DMX Query

Here's a basic example of how to create a time series mining structure and model using DMX:

CREATE MINING STRUCTURE [SalesForecastStructure]
(
    Date DATETIME,
    SalesAmount MONEY
)
WITH
(
    DrillThroughCols = None
);

CREATE MINING MODEL [SalesForecastModel]
ON SalesForecastStructure
WITH
(
    Modeling_Type = 'ARIMA',
    Maximum_Slots = 5,
    Include_Slots_In_Specification = False
);

Best Practices

• Data Cleaning: Ensure your time series data is clean, with no missing values or outliers that could skew results.
• Data Transformation: Consider transformations like differencing or logarithms to stabilize variance and achieve stationarity.
• Model Evaluation: Always evaluate the performance of your model using appropriate metrics (e.g., RMSE, MAE).
• Parameter Tuning: Experiment with different algorithm parameters to find the optimal configuration for your specific dataset.