 Open Access
 Total Downloads : 2030
 Authors : Gundalia Manoj J, Dholakia M. B.
 Paper ID : IJERTV1IS6301
 Volume & Issue : Volume 01, Issue 06 (August 2012)
 Published (First Online): 30082012
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Prediction of maximum/minimum temperatures using Holt Winters Method with Excel Spread Sheet for Junagadh Region
Gundalia Manoj j. Dholakia M. B.
Ph. D. Scholar Joint Director
Gujarat Technological University, Ahmedabad Directorate of Technical Education, Gandhinagar
Abstract
Temperature Time Series data are used as major input in many rainfallrunoff models. Temperature prediction is a temporal and time series based process and involve when insufficient data availability. Temperature time series often consist of periodic patterns, i.e. patterns that repeatedly occur in defined periods over time, Holt Winters method is suitable to predict such meteorological process. Microsoft Excel is user friendly and most popular spread sheet program.
In this paper, in order to predict maximum and minimum daily temperature time series of Junagadh region with Holt Winters method using excel spread sheet is proposed. The performance of the method is measured by three standard error measures MSE, MAPE and MAD which are found well within the acceptable limits. Results shows the maximum temperature time series exhibit less fluctuation and gave better results as compared to minimum temperature time series.
Keywords
Maximum/minimum temperatures; Time Series Prediction; Triple Exponential Smoothing (Holt Winters).

Introduction
Forecasting is a phenomenon of knowing what may happen to a system in the next coming time periods [22]. The goal is to observe or model the existing data series to enable future unknown data values to be forecasted accurately [10]. As weather is a continuous, dataintensive and dynamic process, the parameters required to predict temperature are enormously complex such that there is uncertainty in prediction even for a short period [1].One way of classifying forecasting problems is to consider the timescale involved in the forecast. Short, medium and longterm are the usual categories but the actual meaning of each will vary according to the situation that is being studied. Exponential smoothing technique is one of the most important quantitative techniques in forecasting. The accuracy of forecasting of this technique depends on exponential smoothing constant.
Once data have been captured for the time series to be forecasted, the analysts next step is to select a model for forecasting. Various statistical and graphic techniques may be useful to the analyst in the selection
process. Three tools for assessing the autocorrelation of a time series are (i) the time series plot, (ii) the lagged scatter plot, and (iii) the autocorrelation function. The best place to start with any time series forecasting analysis is the lagged scatter plots of the time series to be forecasted. A sequence plot is a graph of the data series values, usually on the vertical axis, against time usually on the horizontal axis. The purpose of the lagged scatter plot is to give the analyst a visual impression of the nature of the time series. This visual impression should suggest to the analyst whether there are certain behavioural components present within the time series. A lag plot checks whether a data set or time series is random or not. Random data should not exhibit any identifiable structure in the lag plot. Non random structure in the lag plot indicates that the underlying data are not random. The presence/absence of trend and seasonality components can help the analyst in selecting the model with the potential to produce the best forecasts.
Excel is now the most popular spread sheet program and most people have it on their computers. In Rahmbow and Klimbergs 200 studies of forecasting practices, the leading application software was overwhelmingly Excel (90%). Excel is an electronic spread sheet developed by Microsoft; this computer application program simulates a physical spread sheet by capturing, displaying and manipulating data arranged in rows and columns. The main advantage of Excel is its versatility and functionality when you are doing any type of model. It is a much simpler program than a database program. For these reasons, and to avoid the need to purchase specialized forecasting software, the Excel spread sheet program was selected for use in this paper
The Solver is an addin for Microsoft Excel which is used for the optimization and simulation of numerical models. It solves complex linear and nonlinear problems and can also be used in conjunction with VBA to automate tasks. It can solve problems by enabling a Target cell to achieve objectives.

Data Collection
Maximum/minimum daily temperature data were collected from Agro meteorological Cell, Department of Agronomy, College of Agriculture, Junagadh Agricultural University, Junagadh (Gujarat). Most of the rainfallrunoff models have needed data from
month of June to month of October for a year. Maximum daily temperature (0C) data from June to October for period of 1984 to 2010 and Minimum daily temperature (0C) data from June to October for period of 1987 to 2010 were available. In order to complete data set from June to October for period of 1980 to 2010, prediction of maximum temperature time series (MXTTS) for period 1980 to 1983 and minimum temperature time series (MNTTS) for period 1980 to 1986 are needed.

Selection of Method
Choosing an appropriate model or class of models is as much an art as a science. There is no single approach that is best for all situations, but it is possible to lay down some general guidelines. The analyst will generally have to (i) get as much background information as is necessary, (ii) assess costs, (iii) clarify objectives, and (iv) have a preliminary look at the data. Many of the above points are amplified in chapter 1of [12], whose main example discusses the difficulties involved when trying to model the dynamics of deforestation in the Amazon region.[11] provided some simple rules based on the variances of differenced time series for choosing an appropriate exponential smoothing method. [27] compared these rules with others proposed by [8] and an approach based on the BIC. [14] also proposed an information criterion approach, but using the underlying state space models.
The choice of suitable forecasting procedure depends on the properties of the data or the objectives of the study [17], [9].The graph should show up important features of the data such as trend, seasonality, outliers, smooth changes in structure, turning points and/or sudden discontinuities, and is vital, both in describing the data, in helping to formulate a sensible model and in choosing an appropriate forecasting method. A lag plot of mean minimum and mean maximum temperatures of available data series are shown in Fig.1 and Fig.2 respectively. Plots show tight clustering of points along the diagonal. This is the lag plot signature of a process with strong positive autocorrelation. Such processes are highly nonrandom and there is strong association between an observation and a succeeding observation. In short, if you know Xi1 you can make a strong guess as to what Xi will be.
The introduction of a class of statespace models

Holt Winters Method
Twentyfive years ago, exponential smoothing methods were often considered a collection of ad hoc techniques for extrapolating various types of univariate time series. Although exponential smoothing methods were widely used in business and industry, they had received little attention from statisticians and did not have a welldeveloped statistical foundation. These methods originated in the 1950s and 1960s with the work of [5], [6], [13] and [28]. [23] Provided a simple but useful classification of the trend and the seasonal patterns depending on whether they are additive (linear) or multiplicative (nonlinear). [21] was the first to suggest a statistical foundation for simple exponential smoothing (SES)by demonstrating that it provided the optimal forecasts for a random walk plus noise. Further steps towards putting exponential smoothing within a statistical framework are provided by [5], [25], and [3], [4], who showed that some linear exponential smoothing forecasts arise as special cases of ARIMA models. However, these results did not extend to any nonlinear exponential smoothing methods. Exponential smoothing methods received a boost from two papers published in 1985, which laid the foundation for much of the subsequent work in this area. First, Gardner (1985) provided a thorough review and synthesis of work in exponential smoothing to that date and extended Pegels classification to include damped trend. This paper brought together a lot of existing work which stimulated the use of these methods and prompted a substantial amount of additional research. Later in the same year, [26] showed that SES could be considered as arising from an innovation state space model (i.e., a model with a single source of error). Although this insight went largely unnoticed at the time, in recent years it has provided the basis for a large amount of work on state space models underlying exponential smoothing methods.
Triple Exponential Smoothing (Holt Winters Method) method is appropriate when trend and seasonality are present in the time series. It decomposes the times series down into three components: base, trend and seasonal components.
Ft = (Yt / Stp) + (1) (Ft1 + Tt1) (01)
St = (Yt / Ft) + (1) Stp (02)
Tt = (Ft – Ft1) + (1) Tt1 (03)
underlying exponential smoothing methods enabled
W = (F + mT ) S
(04)
them to enjoy the advantages that forecasting
procedures based on a proper statistical model have
t + m t
Where,
t t+mp
[16], [15], [24], [2].Therefore, Holt winters method is appropriate and selected for prediction of these time series.Ft= smoothed value of the level of series for period t Ft1 = smoothed value for period t1
Yt = actual value in period t
Tt = trend estimate
St = seasonality estimate
= Smoothing constant for the data (0< <1)
= smoothing constant for the trend estimate (0< <1) = smoothing constant for seasonality estimate (0<
<1)
p = number of periods in seasonal cycle
m = number of periods ahead to be forecast Wt+m=Winters forecast for m periods into the future

Methodology
The main aim of the present study is to predict maximum and minimum temperature time series for Junagadh district (Gujarat).To start Holt Winters method, there is need F1, T1, and a seasonal factor S1for each period in the cycle. Cyclic period of seven year have been considered for minimum temperature time series and that of four years for maximum temperature time series based on time plot graphs observation and periods to be forecast into the future. For initialization, collect p (Cyclic period) observations and estimates the seasonal factor using equation [05].
Si = yi [ (1/p)(y1 + y2 + y3 ++yp)] (05)
Enter the maximum temperature of day 01of June from year 2010 to 1984 in column Yi of excel spread sheet. Assume Fp = yp / Sp and Tp = 0 and initialized values of Fi and Si as shown in Fig. 3 and 4.The equations (01),
(02) and (03) are formulated in cells C7, D7 and E7as shown in Fig. 5, 6 and 7 respectively. Cell G7, H7 and I7 show the MAD, MSE and MAPE respectively. To optimize values of smoothing constant , and , mean value of MAD should be minimized. Cell J5 is the Invoke Excel Solver to minimize the MAD. Enter locations of parameters , and with constraints in solver dialogue box Fig. 8. Click the Solve button. Solver will start to optimize the parameters , and and Keep the Solver solution. The solution is instantly found and the optimized values of , and appear in cell J2, K2 and L2 respectively Fig. 9. To predict values of maximum temperatures for year 1983 to 1980, simply fill down the formula in cell F30 to cell F33Fig. 9. The graph shows the nature of existing and predicted values Fig. 10. Similar methodology is applied for prediction of minimum temperature time series.

Goodness of Fit
Accuracy can be defined as goodness of fit or how well the forecasting model is able to reproduce data that is already known [20]. This study used three standard error measures: mean squared error (MSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD).
MAD is the average of the absolute value of the error without regard to whether the error was an overestimate or underestimate [18], equation (06) illustrate the MAD formula.
[06]
MSE is a measure of dispersion of forecast errors; statisticians have taken the average of the squared individual errors. Interpreting the MSE value can be misleading; for the mean squared error will accentuate large error terms. Equation (07) describes the MSE measurement.
(07)
MAPE is regarded as a better error measurement than MSE because it does not accentuate large errors. Equation (08) illustrates the MAPE formula. MAPE is not, dependent on the unit of measurement.
(08)
x 100%
For all three measures, the smaller the value, the better the fit of the model. MAD, MSE and MAPE are calculated as an average of the values computed in column G, H and I respectively Fig. 3. A scale to judge the accuracy of the model based on the MAPE measure, developed by [19].MAD and MSE measure is that there is no context to indicate whether the model is good or not. Using MAPE, and applying Lewiss scale, provides some framework as shown in Table 1 to judge the model.

Results and Discussion
The main aim of the present study is to predict maximum and Variations of the triple exponential smoothing techniques were applied to maximum and minimum temperature time series data, the results of which are discussed in this section. The tests were conducted for 153 days on (MXTTS) from year 1984 to 2010 and prediction period were from year 1980 to 1983 while on MNTTS from year 1987 to 2010 and prediction period were from year 1980 to 1986. The mean value of smoothing constants , and for maximum/minimum daily temperature series are obtained as shown in Table 2. The difference in mean values for both the series are found more in and less in . Series are more diverge each other in trend and cyclic patterns. Maximum, minimum and mean values of all three measures MAD, MSE and MAPE are computed and presented in Table3.Mean values of
MAD, MSE and MAPE of MNTTS are less than that of MXTTS. Standard deviation of MAD, MSE and MAPE of MXTTS are less than that of MNTTS.

Conclusions
The Triple Exponential smoothing (HoltWinters Method) is widely used technique to predict the time series. It is used when the data exhibits both trend and seasonality. In this study Triple exponential technique is applied on maximum/minimum temperature time series using excel spread sheet. Values of smoothing constants , and are optimized by minimizing MAD using solver tool of Microsoft excel.
Mean values of smoothing constants , and for (MXTTS) are found less than that of (MNTTS). This suggests that Prediction of (MXTTS) more depends on previous observations than current observations. Mean MAPE value of (MXTTS) is found greater than that of (MNTTS), but standard deviation is low. Out of 153 observations of MAPE only 3 observations are found with MAPE greater than 10 in (MXTTS) while 19 observations are found that of (MNTTS). This indicates that (MXTTS) exhibit less fluctuations and gave better results as compared to (MNTTS).

Acknowledgement
We express sincere thanks to Aro meteorological Cell, Department of Agronomy, College of Agriculture, Junagadh Agricultural University, Junagadh (Gujarat) for providing maximum/minimum temperature data.

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Table 2. Values of smoothing constants
Parameters
Max. Temp Time Series
Min. Temp. Time Series
Mean
0.0270
0.0303
Mean
0.0723
0.1238
Mean
0.2812
0.4568

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Table 3. Measures of errors 

Parameters 
Max. Temp Time Series 
Min. Temp. Time Series 

MAD 
Max 
3.4303 
3.5446 

Min 
1.2912 
0.5059 

Mean 
2.0914 
1.2796 

Stdev 
0.4195 
0.6192 

MSE 
Max 
16.8096 
17.3642 

Min 
2.9674 
0.4435 

Mean 
7.6313 
3.3867 

Stdev 
2.8467 
3.3492 

MAPE 
Max 
10.4488 
18.9432 

Min 
3.5394 
2.1047 

Mean 
6.3902 
5.6224 

Stdev 
1.3088 
3.5366 

GUNDALIA MANOJ J. earned his B.E. in Civil Engineering from M.S. University and did M.E. in Civil (Water Resources Management) Engineering from Gujarat University. He has presented and published 3 research papers in national conference and 2 published in international journal. He has 14 years field experience, 4 years teaching experience and currently he is Assistant Professor in Dr. Subhash Technical Campus, Junagadh, (Gujarat), INDIA.
Table 1. A scale of Judgment of Forecast Accuracy (Lewis) 

MAPE 
Judgment of Forecast Accuracy 

Less than 10% 
Highly Accurate 

11% to 20% 
Good Forecast 

21% to 50% 
Reasonable Forecast 

51% or more 
Inaccurat Forecast 
Fig.1 Lag plot of Mean Minimum Temperature for period 1987 to 2010
Fig. 2 Lag plot of Mean Maximum Temperature for period 1984 to 2010 
Fig. 3 Initialization of Fi 
Fig. 4 Initialization of Si 
Fig. 5 Formulation of equation [01] 
Fig. 6 Formulation of equation [02] 
Fig. 7 Formulation of equation [03]
Fig. 8 Formulation of equation [01] 
Fig. 9 Optimized parameters , and and predicted values of maximum temperature 
Fig. 10 Existing and predicted values of maximum temperature time series for day June 01