Time Series Analysis Concepts
Time series analysis is a statistical method that deals with time-ordered observations or measurements. It involves analyzing time series data to extract meaningful statistics and other characteristics of the data. The primary goal is to understand the underlying structure of the data, identify patterns, and potentially forecast future values.
What is Time Series Data?
Time series data is a sequence of data points collected, recorded, or observed over time. These data points are typically recorded at successive, equally spaced points in time. Examples include:
- Stock prices over days, months, or years.
- Monthly sales figures for a product.
- Daily temperature readings.
- Annual GDP growth rates.
- Website traffic over hours or minutes.
Components of a Time Series
A typical time series can be decomposed into several components:
- Trend: The long-term increase or decrease in the data. It represents the general direction of the data over an extended period.
- Seasonality: Patterns that repeat over a fixed period, such as daily, weekly, monthly, or yearly cycles.
- Cyclicality: Fluctuations that are not of a fixed period, often related to business or economic cycles. These are longer-term than seasonality.
- Irregularity (or Noise): The random, unpredictable variations in the data that remain after accounting for trend, seasonality, and cyclicality.
Key Goals of Time Series Analysis
- Description: Understanding the characteristics and patterns within the historical data.
- Explanation: Identifying factors that influence the time series.
- Forecasting: Predicting future values based on historical patterns.
- Control: Using forecasts to manage and influence future outcomes.
Common Time Series Models in SQL Server Analysis Services
SQL Server Analysis Services (SSAS) provides algorithms and tools to perform time series analysis. The primary algorithm used is the ARIMA (AutoRegressive Integrated Moving Average) model.
ARIMA Models
ARIMA models are powerful statistical methods for time series forecasting. They are defined by three parameters (p, d, q):
- AR (AutoRegressive) - p: The number of lag observations included in the model.
- I (Integrated) - d: The number of times the raw observations are differenced.
- MA (Moving Average) - q: The size of the moving average window.
SSAS implements the ARIMA algorithm to model and forecast time series data, automatically selecting the best ARIMA parameters for your data.
Steps in Time Series Analysis with SSAS
- Data Preparation: Ensure your data is correctly formatted with a time dimension and a measure to be analyzed.
- Model Training: Use the SSAS mining wizard to create a time series mining structure and train the ARIMA model on your historical data.
- Model Evaluation: Assess the accuracy and performance of the trained model.
- Forecasting: Use the trained model to predict future values.