GLOBAL TEMPERATURE
FORECASTING FOR AIRLINES Group B4
Ajeet Singh Yadav - 6191053
Abhirup Bhabani - 61910410
Gayatri Mohana Chandran - 61910222
Jayadeep P - 61910715
Rohan Patra - 61910121
Sandeep Ankala - 61910166
Executive Summary
Problem Statement: Extreme temperatures affect flight schedules and the maintenance of
aircrafts. One of the major concerns for major airliners around the world is how temperature
changes affect the functioning of aircrafts ultimately resulting in loss of revenue and increasing
costs. As airliners have a maximum or a minimum operating temperature beyond which they would
ground flights or reschedule, having information about how the temperatures would be brings in a
lot of value and cost savings.
Hence, the analysis aims to provide a forecast of both the maximum and minimum temperatures
monthly for aircrafts so that they can better schedule their flights. As major airliners have global
operations the analysis has been done for three major cities: Tokyo, Paris and Los Angeles. The
model provides a 12-month forecast as airlines are well equipped with the information. Also, as
they require the data well in advance to plan the schedules the model will provide a 6-month lead
time.
Data Description: The data that is available are for three major cities: Tokyo, Paris and Los
Angeles. The locations have been chosen strategically as they have one of the largest airline traffic
in the world and have a number of airline operations and service centers. Data has been collected
from 2000 to 2012 and consists of both maximum and minimum temperature for each city. There
is also monthly data for each of the year (without any missing values and in degree Celsius) that
will be used for the analysis. Below are the plots for the temperatures for each city.
Each of the plots have evident time series components present (Appendix 1). In addition to noise
and level at some points for the plots, all of them have seasonality and a slightly varying level. All
these factors will be considered while analyzing the data and then decide on which model would
be used to predict the temperatures. The data has been obtained from website of the Department
of Statistics at The University of Auckland.
Forecasting Goal & Model Used: The goal is to forecast the maximum and minimum monthly
temperatures for each of the cities over a period of 12 months. The model also accounts for the 6-
month lead time that needs to be provided for the airliners. As there are different time series
components present in plot for the three cities, the analysis has shown that Holt-Winters Additive
model would be used for Los Angeles, while Linear Regression model will be used for Tokyo and
Paris. The RMSE and MAPE with the seasonality naïve prediction have been compared with those
of the chosen models and it is found that they perform better on both prediction and validation
data.
Conclusions & Recommendations: The 18-month forecast that is provided to the airliners can be
used to make any schedules or operations optimizations in advance. This not only provides a better-
informed decision for the airliners but also ensures that revenues leakages and cost overruns are
avoided and saved. Airliners can use the data and compare them with the operating temperatures
of their aircrafts and then make decisions.
However, there are some implications when there the temperatures are under or over-forecasted.
For each half a degree of Celsius predicted below, there is another minute added to the flight. This
adds to extra time on air increasing fuel consumption. Also, if there is data for daily temperatures,
predictions can be made on a daily basis that will be of higher value to the stakeholders. Since
airline prices are dynamic and chance regularly based on how the temperatures fluctuate (as
mentioned earlier differing temperatures affect flying times and eventually change fuel prices),
providing accurate prediction is important. To avoid the costs of wrong forecasting, the models
will be run every month and provide updated forecasts for the airliners.
Technical Summary
Data Preparation: There is no data missing in the series. However, the format of the date provided
needed to be changed as it was not in the format that is convenient for analysis and for
visualization. The date has been changed to MM-YYYY format which makes it convenient for the
analysis. Further, as we require an 18-month forecast, the data has been partitioned to 132 rows as
the training period and 18 rows as the validation period and various models were tested upon it
that will be discussed below. For linear regression there were three additional columns created for
trend, trend^2 and seasonality (dummy variables to account in the model).
Finally, as we are re-forecasting every month, we have included an additional row of new data
(starting from July 2012 and so on) and then re-forecasted the temperatures.
Forecasting Models Used: To account for the various time series components and the visual
analysis made, Seasonal Naïve, Exponential Smoothing, Holt Winters (No Trend & Additive) and
Linear Regression models have been selected for the analysis. Below are the various observations
made in regard to the various models used and why a particular model was selected based on
benchmarks.
• Los Angeles (Model Selected – Holt Winters Additive Model)
Los Angeles has a comparatively different plot of the temperatures. Although there is seasonality,
the time series has a lot of noise that makes it challenging to choose the right model. Linear
regression model made an impressive performance on the minimum temperatures but did not do
well for the maximum temperatures as there was a lot of noise that the model could not take into
consideration. Holt-Winters performed better (Additive performed better than No Trend) than the
other models for minimum temperatures.
Hence, Holt Winter’s model has been selected to forecast the temperatures for models in Los
Angeles data. The number of periods chosen was 12 with alpha, beta and Gamma values 0.2, 0.15
and 0.05 respectively. (Appendix 2)
• Tokyo (Model Selected – Linear Regression Model)
Due to the seasonality in the data, exponential and double exponential smoothing did not perform
well on the data. Holt-Winter's model was able to capture the seasonality and hence produce better
results, but it did not perform better than the Seasonal Naïve benchmark. Amongst all the models,
linear regression performed the best where the time periods were a running index and the months
were categorical variables to account for seasonality. (Appendix 3)
• Paris (Model Selected – Linear Regression Model)
Paris has proper seasonality, but the time series has a lot of noise that makes it challenging to
choose the right model like the Los Angeles data. Using exponential smoothing to cancel the noise
didn’t help much because we got a very high RMSE of 6.53 for max temperature and 4.64 for min.
Linear regression model made an impressive performance on both max and min temperatures than
the other models. Hence, we went ahead with the same model. (Appendix 4)
Performance of Models: The data below shows the performance of various models and the
highlighted ones have been picked. (Appendix contains all the forecasts)
Tokyo Metric Max Min
Seasonal Naïve RMSE 1.54
MAPE 17.32
Exponential Smoothing
RMSE 9.71
MAPE 93.6
Holt-Winters Additive
RMSE 1.98
MAPE 42.93
Holt-Winters No Trend
RMSE 1.19
MAPE 22.82
Linear Regression
RMSE 1.2
MAPE 25.63
Paris Metric Max Min
Seasonal Naïve RMSE 17.43 9.27
MAPE 18.5% 111.0%
Exponential Smoothing
RMSE 6.53 4.64
MAPE 41.7% 80.3%
Holt-Winters Additive
RMSE 8.50 5.43
MAPE 43.7% 73.2%
Holt-Winters No Trend
RMSE 6.86 4.81
MAPE 40.8% 75.4%
Linear Regression
RMSE 1.91 1.46
MAPE 12.6% 67.1%
Los Angeles Metric Max Min
Seasonal Naïve RMSE 1.92 1.35
MAPE 6.9 19.47
Exponential Smoothing
RMSE 2.43 3.23
MAPE 9.69 23.06
Holt-Winters Additive
RMSE 2.13 0.78
MAPE 8.25 2.29
Holt-Winters No Trend
RMSE 2.86 0.78
MAPE 9.2 5.14
Linear Regression
RMSE 1.32 0.87
MAPE 122 5.32
Appendix:
Figure 1: Visual Analysis to check for seasonality & other time series components:
• Los Angeles:
• Tokyo:
• Paris
Figure 2:
Forecasting Temperatures periodically for following months (Los Angeles):
Forecast of Max and Min until July 2012
Forecast of Max and Min until August 2012
Figure 3:
Forecasting Temperatures periodically for following months (Tokyo):
Forecast of Max and Min until July 2012
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Tokyo Maximum Temperature
Tokyo Max Prediction: Tokyo Max
Figure 4:
Forecasting Temperatures periodically for following months (Paris):
Forecast of Max and Min until July 2012
Forecast of Max and Min until August 2012
