# Time Series Forecasting

```TIME SERIES FORECASTING
Presented By,
1. Nishant Nagle
2. Piyush Suryawanshi
3. Saurabh Patil
4. Sohan Kapse
5. Anuj Umare
6. Bhakti Datarkar
1
INTRODUCTION

AIM- To carefully collect and rigorously study past observations
to develop appropriate model

Model used to generate future values of the series i.e. forecast

Act of predicting future by understanding the past

Used in –

Statistics

Econometrics

Weather Forecasting

Earthquake Prediction
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TIME SERIES FORECASTING

Definition:
A time series is a set of observations on the values that a variable
takes at different times.

Univariate and Multivariate

Regular Time Intervals

Monthly ( Consumer Price Index)

Weekly (Money Supply)

Quarterly (GDP)

Annually (Government Budget)
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COMPONENTS OF TIME SERIES


TrendUpward Trendseries relating to population growth
Number of houses in a city

Downward TrendMortality rates

Seasonal-
Sales of ice-cream increase in summer
Sales of woolen clothes increase in winter
Quarterly GDP series for India
4
TIME SERIES COMPONENTS

CyclicalWavelike Pattern
Longer period Usually for 2 or more years
e.g. Business Cycle consists of 4 phases
5
TIME SERIES COMPONENTS

Random
These are sudden changes occurring in a time series which are
unlikely to be repeated.
Eg : Floods,fires,earthquakes,revolutions and strikes etc are the
root causes.
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DESIGN OF FORECASTING SYSTEMS
• Develop the forecasting logic by identifying the purpose, data
and models to be used.
STAGE 1
• Establish control mechanisms to obtain reliable forecasts
STAGE 2
• Incorporate managerial considerations in using the forecasting
system
STAGE 3
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DEVELOPING THE FORECASTING LOGIC
Identify:
1) The purpose of forecast.
2)The time horizon.
3)Type of Data needed.
Start
Identify a suitable technique:
1) Collect or Analyse past data
2) Select an appropriate model
Develop the Forecasting logic:
1)Establish Model Parameters
2) Build the model
Stop
Satisfactory
historical data
8
CLASSIFICATION OF MODELS FOR
FORECASTING
• Extrapolative methods
o Making use of Past data to prepare future estimates
o Useful for Short term purpose in an organisation
o Moving Average
• Casual Models
o Analyse the data from the viewpoint of a cause-effect relationship
o Econometric Model
o SPSS model helps in developing casual model
• Based on Subjective Judgement
o Based on set of Quantitative and Qualitative data
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Applications
• Inflation rate or unemployment rate or net inflow of
foreign funds
• Demand of products like automobiles, soft drinks etc.
• Forecasting of Gold and Silver prices by Merchants
• Budgetary Analysis
• Stock Market Analysis
• Process and Quality Control
• Inventory Studies
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CONCLUSION
• Proper selection of model is extremely crucial
for forecasting
• Fast growing area of research provides many
scopes for future works, one of them is
combining approach
• A sophisticated forecasting system is not
enough; managers must use the forecast
obtained from the system in the context of
external information
11
THANK YOU
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