This document discusses forecasting techniques in time series analysis and causal models. It describes time series models as analyzing a time-ordered sequence of observations over regular intervals to identify trends. These include simple exponential smoothing, which weights older data less and newer data more, and moving averages, which use an average of past periods to forecast the next period. Causal models are based on relationships between dependent and independent variables, assuming past trends will continue influencing future variables. Linear regression is provided as an example causal model that fits a line to measure the effect of a single independent variable.

2.  Uses the range of mathematical
and statistical techniques used
to analyse data.
 2 Categories:
-Time series models
-Casual models

3. A time ordered sequence of
observations taken at regular
intervals over a period of time

4. Trend

8.  The forecast of demand for the next period
is assumed to be equal to the actual demand
in the current period.

9.  Uses an average of ‘n’ most recent periods of
demand data to forcast the next period
demand.
 For example, it is often used in technical
analysis of financial data, like stock prices,
returns or trading volumes. It is also used in
economics to examine gross domestic
product, employment or other
macroeconomic time series.

10.  In exponential smoothing older data is given
progressively-less relative weight
(importance) whereas newer data is given
progressively-greater weight.
 Next period’s forecast=this period’s
forecast+α (this period’s actual demand –this
period’s forecast)

11.  Based on the assumption that the variable
to be forecast (dependent variable) has
cause-and-effect relationship with one or
more other (independent) variables.

12.  The factors responsible for the past trends in
variables to be projected (e.g. sales and
demand) will continue to play their part in
future in the same manner and to the same
extend as they did in the past in determining
the magnitude and direction of the variable.

14.  A technique in which a straight line is fitted
to a set of data points to measure the effect
of a single independent variable. The slope
of the line is the measured impact of that
variable.

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