time series part 2

• There are 3 main components influencing time series data:

  1. Trend

  2. Seasonality

  3. Irregular (random) component
• The trend is defined as the “long term” movement in a time

  series without calendar-related and irregular effects, and is a

  reflection of the underlying level of influences such as

  – population growth,

  – price inflation

  – general economic changes.
• Seasonality in a time series can be identified by regularly

  spaced peaks and troughs which have a consistent direction

  and approximately the same magnitude every year, relative to

  the trend
• The irregular component is what remains after the seasonal and trend components of a time series have been estimated and removed
• Irregular effect is unpredictable in terms of

  – Timing

  – Impact

  – Duration
• The most popular smoothing and forecasting techniques are:

  • Moving average (MA) method

  • Exponential smoothing

  • Least Squares (LS) method
• The mean model assumes that the best predictor of what will happen

  tomorrow is the average of everything that has happened up until now.
• The random walk model assumes that the best predictor of what will happen

  tomorrow is what happened today, and all previous history can be ignored.
• Moving average MA is a series of arithmetic means computed over time such that each mean is calculated for a sequence of observed values having the particular length
• Moving averages are commonly used with time series data to smooth out short-term fluctuations and highlight longer-term trends or cycles.
• Whereas in the simple moving average the past observations are weighted

  equally, exponential functions are used to assign exponentially decreasing

  weights over time.