Time Series
• a set of numerical values of some variable obtained at regular period over time. ance
• The series is usually tabulated or graphed in a manner that readily conveys the behaviour of the variable under study, conveys
- a series of data points indexed (or listed or graphed) in time order. (Chronological arrangement of data)Time Series
• a set of numerical values of some variable obtained at regular period over time. ance
• The series is usually tabulated or graphed in a manner that readily conveys the behaviour of the variable under study, conveys
- a series of data points indexed (or listed or graphed) in time order. (Chronological arrangement of data)
Component of Time Series
Trend
- a steady tendency of either upward or downward movement in the average (or mean) value of the forecast variable over time.
• When observations are plotted against time, a straight line describes the increase or decrease in the time series over a period of time.
Component of Time Series
Cycles
- An upward and downward movement in the variable value about the trend time over a time period usually Variab value.
• A business cycle may vary in length, usually more than a year but less than 5 to 7 years.
• The movement is through four phases: from peak (prosperity) to contradiction (recession) to trough (depression) to expansion (recovery or growth)
Component of Time Series
Seasonal
a special case of a cycle component of time series
• fluctuations are repeated usually within a year (e.g. daily, weekly, monthly, quarterly) with a high degree of regularity.
- patterns tend to be repeated from year to year.
• For example, average sales for a retail store may increase greatly during festival seasons.
Component of Time Series
Irregular . are rapid charges or bleeps in the data caused by short term unanticipated and non-recurring factors.
• Irregular fluctuations can happen as often as day to day.
Application/ Use
• To Predict Future
• Used to identify fluctuations in economic and business Helps in evaluation of current achievements
• Used in pattern pattern recognitions, weather forecasting, rainfall measurements etc.
• Used in sales forecasting
Multiplicative Model
• The actual values of a time series, Y can be found by multiplying four components at a particular time periodiponents on the time
• The effect of four components on the time series is interdependent.
• Y =T × C × S × I ← Multiplicative model
• The multiplicative model is appropriate in situations where the effect of C, S, and I is measured in relative sense and is not in absolute sense.
The geometric mean of C, S, and I is assumed to be less than one.
Additive Model
• In this model, it is assumed that the effect of various components can be estimated by adding the various components of a time-series. • Y=T + C + S + I ← Additive model
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