Time Series and Forecasting

Running Head: TIME SERIES AND FORECASTING
Time Series and Forecasting
Name of the Student
Name of the University
Author Note

1TIME SERIES AND FORECASTING
Table of Contents
1.0 Introduction..........................................................................................................................2
2.0 Sales Forecast Methods........................................................................................................3
2.1 Simple Exponential Smoothing Method..........................................................................3
2.2 Holt’s Method..................................................................................................................4
2.3 Winter’s Method..............................................................................................................5
3.0 Autocorrelations...................................................................................................................7
4.0 Regression Analysis.............................................................................................................9
5.0 Conclusions........................................................................................................................10
References................................................................................................................................11
Appendix..................................................................................................................................12

2TIME SERIES AND FORECASTING
1.0 Introduction
For the purpose of this project time series data has been collected from world bank.
The data on average monthly rainfall in Malaysia for the 9-year period from 2007 to 2015 has
been collected for this study. A total of 108 data points are available for the analysis. The
analysis has been conducted using the statistical software SPSS version 20.
The original time series is plotted in figure 1. From the figure, it can be seen clearly
that the average monthly rainfall follows a seasonal fluctuation every year.
By seasonal fluctuations it is meant that there is a periodic movement in a time series
where the period is not longer than 1 year. A periodic movement in time series is one which
recurs or repeats at regular intervals of time (or periods) (Montgomery, Jennings and Kulahci
2015).

3TIME SERIES AND FORECASTING
2.0 Sales Forecast Methods
2.1 Simple Exponential Smoothing Method
C.C. Holt first suggested this this method of forecasting in 1958. This method is
usually used to forecast values which do not have any systematic trend and follows non-
seasonal time series data. In reality, the data that are obtained do not usually follow any
seasonal pattern. The non-seasonal effects are measurable and can be removed and thus, the
developed and revised model will be stationary.
In exponential smoothing technique of forecasting, data which is older is given lesser
priority and the data which are new are given more priority. This method is also known as
averaging and is used to forecast values for a shorter term (Ramtirthkar et al. 2016).
The forecast using the simple exponential smoothing method for the year 2016 is
given in table 2.1.
Table 2.1: Forecast Exponential Smoothing
Model Jan
2016
Feb
2016
Mar
2016
Apr
2016
May
2016
Jun
2016
Jul
2016
Aug
2016
Sep
2016
Oct
2016
Nov
2016
Dec
2016
Rainfall
(in
mm)-
Model_
1
Foreca
st
255.5
8
255.5
8
255.5
8
255.5
8
255.5
8
255.5
8
255.5
8
255.5
8
255.5
8
255.5
8
255.5
8
255.5
8
UCL 429.4
6
429.5
5
429.6
4
429.7
3
429.8
2
429.9
1
430.0
0
430.0
9
430.1
8
430.2
7
430.3
6
430.4
5
LCL 81.70 81.61 81.52 81.43 81.34 81.25 81.16 81.07 80.98 80.89 80.80 80.71