Time Series and Forecasting Time Series A time

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Time Series and Forecasting

Time Series and Forecasting

Time Series A time series is a set of statistical observations arranged in chronological

Time Series A time series is a set of statistical observations arranged in chronological order. A time series is a collection of data recorded over a period of time - weekly, monthly, quarterly, or yearly.

Example of Time series Economics - weekly share prices, monthly profits Meteorology - daily

Example of Time series Economics - weekly share prices, monthly profits Meteorology - daily rainfall, wind speed, temperature Sociology - crime figures (number of arrests, etc), employment figures

Utility of Time series analysis It helps in understanding past behaviour It helps in

Utility of Time series analysis It helps in understanding past behaviour It helps in planning future operations It helps in evaluating current accomplishments It facilitates comparison

Components of time series Secular Trend Seasonal Variations Cyclical Variations Irregular Variations

Components of time series Secular Trend Seasonal Variations Cyclical Variations Irregular Variations

Secular Trend is a long term movement in a time series. It is the

Secular Trend is a long term movement in a time series. It is the underlying direction (upward or downward) and rate of change in a time series, when allowance has been made for the other components.

Understanding Secular Trend Basic tendency of production, sales, income, employment, etc. • Population Change

Understanding Secular Trend Basic tendency of production, sales, income, employment, etc. • Population Change • an aging population (which will tend to have different spending and savings habits than a younger population) • Technological Progress • the expansion of a particular technology (such as the Internet) • heavy reliance on certain commodities (like oil) • Large scale shift in customer tastes

Understanding Secular Trend Linear and nonlinear trend Definition of long period Long term movement

Understanding Secular Trend Linear and nonlinear trend Definition of long period Long term movement

Secular trend in equity market

Secular trend in equity market

Measurement of secular trend Freehand smooth curve Semi-average method Moving average method Methods of

Measurement of secular trend Freehand smooth curve Semi-average method Moving average method Methods of least squares

Seasonal variations • Periodic movements in business activity which occur regularly every year and

Seasonal variations • Periodic movements in business activity which occur regularly every year and have their origin in the nature of the year itself. • Repeat during a period of 12 months

Factors causing seasonal variations Climate and whether conditions Customs, traditions, and habits

Factors causing seasonal variations Climate and whether conditions Customs, traditions, and habits

Cyclical variations of non-seasonal nature, whose periodicity is unknown. Changes of economy activity as

Cyclical variations of non-seasonal nature, whose periodicity is unknown. Changes of economy activity as a result of recurring causes.

Business Cycle

Business Cycle

Business cycle Do not show regular periodicity Longer than one year (2 to 10

Business cycle Do not show regular periodicity Longer than one year (2 to 10 years) Different set of causes

Irregular Variations Irregular variations refer to such variations which do not repeat in a

Irregular Variations Irregular variations refer to such variations which do not repeat in a definite pattern. Also called erratic, accidental and random. Causes of such variations may be floods, earthquakes, strikes, wars, etc.

Components of a Time Series Additive Model • Y=T+C+S+R Multiplicative Model • Y=T×C×S×R

Components of a Time Series Additive Model • Y=T+C+S+R Multiplicative Model • Y=T×C×S×R

we denote the time series by Y, the secular trend by T, the seasonal

we denote the time series by Y, the secular trend by T, the seasonal movements by S, the long term cyclical movements by C and the irregular or residual component by R

Preliminary adjustments Calendar Variations Population changes Price changes Comparability

Preliminary adjustments Calendar Variations Population changes Price changes Comparability

Measurement of Trend Freehand or Graphic Method Semi-average Method Moving average method Methods of

Measurement of Trend Freehand or Graphic Method Semi-average Method Moving average method Methods of Least Square

Practical 1 Year Sales ( in Rs. Lakhs) 2003 10 2004 13 2005 16

Practical 1 Year Sales ( in Rs. Lakhs) 2003 10 2004 13 2005 16 2006 21 2007 24

Practical 2 Year 1993 1994 1995 1996 1997 1998 1999 2000 No. of Industrial

Practical 2 Year 1993 1994 1995 1996 1997 1998 1999 2000 No. of Industrial Failures 23 26 28 32 20 12 12 10

Forecasting Replenishment Forecasting Replenishment Forecasting Forecasting Replenishment ForecastingForecasting Replenishment Forecasting Replenishment Forecasting Forecasting Replenishment Forecasting
forecasting longterm forecasting middleterm forecasting shortterm forecasting 2forecasting longterm forecasting middleterm forecasting shortterm forecasting 2
Forecasting Forecasting Problems and Methods New product forecastingForecasting Forecasting Problems and Methods New product forecasting
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Introduction Definition Forecasting Technology Technological Forecasting Process ForecastingIntroduction Definition Forecasting Technology Technological Forecasting Process Forecasting
Forecasting Forecasting A Basis of Forecasting In businessForecasting Forecasting A Basis of Forecasting In business
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Statistics Cyclical Time Series Cyclical Forecasting Time seriesStatistics Cyclical Time Series Cyclical Forecasting Time series
Time Series and Forecasting Random Series STEREO XLSTime Series and Forecasting Random Series STEREO XLS
Chapter 17 Time Series Analysis and Forecasting TimeChapter 17 Time Series Analysis and Forecasting Time
MGTSC 352 Finals Notes Forecasting Forecasting Quantitative TimeMGTSC 352 Finals Notes Forecasting Forecasting Quantitative Time
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1000 Series 2000 Series 3000 Series 4000 Series1000 Series 2000 Series 3000 Series 4000 Series
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Time Series Analysis TIME SERIES ANALYSIS Introduction TimeTime Series Analysis TIME SERIES ANALYSIS Introduction Time
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