Example: Summer and winter influence activities (e.g., sports such as skiing), and clothing. The annual calendar influences the variable of interest. Example: Retail sales influenced by holidays. Techniques that should be considered when forecasting seasonal series include classical decomposition, Census X-12, Winter's exponential smoothing, multiple regression, and autoregressive integrated moving average (ARIMA) models (Box-Jenkins methods). Forecasting Technique for Cyclical Data A cyclical effect is as wavelike fluctuation around the trend. Decomposition methods can be extended to analyze cyclical data. Forecasting technique for seasonal data are used in the following circumstances: The business cycle influences the variable of interest. Example: Variables like economic, market, and competition factors. Shifts in popular tastes occur. Example: Shifts like fashions, music, and food.
Example: Summer and winter influence activities (e.g., sports such as skiing), and clothing. The annual calendar influences the variable of interest. Example: Retail sales influenced by holidays. Techniques that should be considered when forecasting seasonal series include classical decomposition, Census X-12, Winter's exponential smoothing, multiple regression, and autoregressive integrated moving average (ARIMA) models (Box-Jenkins methods). Forecasting Technique for Cyclical Data A cyclical effect is as wavelike fluctuation around the trend. Decomposition methods can be extended to analyze cyclical data. Forecasting technique for seasonal data are used in the following circumstances: The business cycle influences the variable of interest. Example: Variables like economic, market, and competition factors. Shifts in popular tastes occur. Example: Shifts like fashions, music, and food.
exploring_data_patterns.docx