1. We make ICT strategies work
Prof. Dr.-Ing. Thomas Bauschert , Dr. Mathias Schweigel, Oleksandr Kryvoshapka
Technische Universität Chemnitz, Detecon International GmbH
Feb 2016
A Framework for Telecommunication
Traffic Demand Forecasting

2. Motivation & Goals
©Detecon
– 2 –
 Extend the forecasting abilities of Detecon
NetWork products
 Create a theoretical and practical basis for
further developments
 Provide implementation of the forecasting
methods
 Learn the basics of forecasting process
 Classify the existing forecasting methods
 Implement some forecasting methods (if
necessary)
 Identify restrictions and conditions of
forecasting for mobile networks
 Perform a research with real operator’s data
Tasks Goals

3. ©Detecon
– 3 –
AuthorQuote
Introduction
"Prediction is very difficult,
especially if it's about the
future.“
“A planning tool that helps management in its attempts to
cope with the uncertainty of the future, relying mainly on
data from the past and present and analysis of trends.”
Niels Bohr (1885-1962)

4. Content
1. Basics of forecasting
2. Forecasting methods
3. Applications constrains
4. Evaluation of results
5. Summary & Outlook
Overview:
©Detecon
– 4 –

5. Basics
©Detecon
– 5 –
 Timing
 Frequency
 Duration
 Short-term (<1 year)
 Medium-term (1-2 years)
 Long-term (>2 years)
 Operation management
 Marketing
 Finance and Risk
management
 Economics
 Industrial process control
 Demography
PropertiesAreas Forecast horizon
Forecasting introduction

6. Basics
©Detecon
– 6 –
ComponentsTime-series
Time-series data
 Usually historical data
consist of a sequence of
observations over time.
 It is called a time-series
data: set of observations x,
recorded at a specific time t.
 There are two types of such
series:
 Discrete time-series
 Continuous time-series

7. Basics
©Detecon
– 7 –
ComponentsTime-series
Time-series components
 Time-series can contain next
components or patterns:
 Trend (T)
 Seasonal variations (S)
 Cyclical variation (C)
 Irregular fluctuations (I)
 Can be combined in different
ways
 Not all components are
present in the time-series
simultaneously

8. Proposed Classification
Forecasting methods
Forecasting methods classification
©Detecon
– 8 –
* - implemented

9. ©Detecon
– 9 –
Pros & ConsMethod overview
Periodic DES and Periodic LinReg
 Most of the methods are
able to handle only trend
component of the time series
data, but not seasonal (e.g.
DES, LinReg).
 Modification –
deseasonalization:
1. Split the data into
unseasonal slices
2. Perform forecasting for
each slice separately
3. Combine the results of
forecasting in the correct
order
 Forecasting equations
remains unchanged
 Only data pre- and post-
processing is changed
Methods overview

10. Application constrains
Aggregation levels
©Detecon
– 10 –
Data scale Forecast horizon (forecast next…)
Hourly
Daily
Weekly
Monthly
Quarterly
Yearly
Hour
Day
Week
Month
Quarter
Year
w3

11. ©Detecon
– 11 –
DescriptionIllustration
Data problem
 Practical forecasting task is
different from theoretical
 Data corruption problems:
 Missing data
 Outliers
 There are two parameters of
data corruption:
 Position
 Size
 Different recovery
techniques proposed:
 Naïve (Copy)
 Averaging
 Interpolation
 “Small” forecast
Application constrains

12. ©Detecon
– 12 –
DescriptionIllustration
Test methodology
 MODEL part used as an
input.
 Period count starts from the
earliest data.
 FORECAST START line
separates present from
future
 FORECAST part used as a
reference for output.
 FH specifies the range of
forecast (e.g. days, weeks)
 MSE value is used as an
accuracy measurement
 Implementation -
Python+Excel
Evaluation of results
FORECAST START
MODEL FORECAST
1 2 3 4 5 6 7
Forecast Horizon (FH)

13. ©Detecon
– 13 –
List of the tests
Performed tests
Evaluation of results
№ Objective Methods
1 Influence of different amount of input data
TES, Periodic DES, Periodic
LinReg
2 Influence of different FH size
3
1 Influence of the position of missing data (recovered)
2 Influence of different recovery techniques
4
1 Hourly data based forecast (FH -168 values)
2 Daily data based forecast (FH - 7 values)
TES, Periodic DES, Periodic
LinReg, DES, LinReg
3 Weekly data based forecast (FH - 1 values) DES, LinReg

14. ©Detecon
– 14 –
ParametersData
TEST 1: Different amount of input periods used
 Input data
 Input - 48-168 hourly
measurements
 Output - 48 hourly values
 Periodicity
 d1 – 24
 Tested methods
 TES
 Periodic DES
 Periodic LinReg
Evaluation of results

15. ©Detecon
– 15 –
ResultsOutput
TEST 1: Different amount of input periods used
 MSEforecast compared.
 An overall slowdown of the
MSEforecast values with the
increment of input data amount.
 Most dramatic decrease –
Periodic DES
 Smallest MSEforecast value –
TES
 7 periods of input data will be
used for the next test scenario.
Evaluation of results

16. ©Detecon
– 16 –
ParametersData
TEST 3.1: Missing data position
 Input data
 Input - 168 hourly
measurements
 Positions of the gap:
– 1st period
– 4th period
– 7th period
 Gap recovered as average of
full input
 Output - 48 hourly values
 Periodicity
 d1 – 24
 Tested methods
 TES
 Periodic DES
 Periodic LinReg
Evaluation of results

17. ©Detecon
– 17 –
ResultsOutput
TEST 3.1: Missing data position
 MSEforecast compared.
 The position of the gap does
matter.
 All three forecasting methods
reacts on the recovered gap with
the growth of MSEforecast value.
 Gap in the last period is the most
corrupting for the MSEforecast
values.
 Gap in the middle is the most
safe for TES and Periodic DES
 Gap in the beginning safe for
Periodic LinReg.
Evaluation of results

18. ©Detecon
– 18 –
ParametersData
TEST 4.1: Hourly forecast
 Input data
 Input - 336 hourly
measurements
 Output - 168 hourly values
 Periodicity
 d1 – 24
 w1 - 168
 Tested methods
 TES
 Periodic DES
 Periodic LinReg
Evaluation of results

19. ©Detecon
– 19 –
ResultsOutput
TEST 4.1: Hourly forecast
 MSEforecast and evaluation
speed was compared.
 TES performing worse in w1 than
in d1
 Periodic DES was improved in
case w1
 Periodic LinReg almost does not
change. The fastest method
(x1000)
Evaluation of results

20. ©Detecon
– 20 –
ParametersData
TEST 4.2: Daily forecast
 Input data
 Input - 14 daily
measurements
 Output - 7 daily values
 Periodicity
 w2 - 7
 Tested methods
 TES
 Periodic DES
 Periodic LinReg
 DES
 LinReg
Evaluation of results

21. ©Detecon
– 21 –
ResultsOutput
TEST 4.2: Daily forecast
 MSEforecast and evaluation
speed was compared.
 Cases d1 and w1 are just
aggregated to the busy-hour-per-
day scale results of previous test
 MSEforecast value for TES and
Periodic DES is noticeably
reduced.
 LinReg – the smallest
MSEforecast value. The fastest
method.
Evaluation of results

22. ©Detecon
– 22 –
DecisionsTests
TESTs Results
 Conclusion out of all test are
relevant only for used test data
set.
 TES – the most universal method
 Periodic LinReg – the fastest
method
 Combination of the methods can
increase the chances for making
the correct decision.
Evaluation of results
TEST 1 More data - better
the forecast
Test leader: TES
TEST 2
Forecast reliability
decreases with FH
duration
Test leader: TES
TEST 3
Edges of the data
is the most
influencing for
forecasting
accuracy
Test leader:
TES +
Recover by the
neighboring
average
TEST 4
If enough data
available, the same
aggragation level
as FH should be
used.
Test leader:
Periodic LinReg

23. Summary & Outlook
Summary & Outlook
©Detecon
– 23 –
The work can be extended in a several directions:
 Integration of the methods into the NetWorks Forecast
 New forecasting methods investigation
 Automatic constrains (data problem) solving algorithms
 Additional test scenarios to generalize the knowledge
 The basics of forecasting process were studied
 The criteria for classifying forecasting methods were
illustrated.
 New classification were proposed, focusing on
quantitive methods grouped by internal used statistical
mechanism.
 The modifications of two existing methods were
proposed: Periodic DES and Periodic LinReg.
 Four important factors for selecting appropriate
forecasting methods were highlighted.
 A guideline for data recovery after missing data and
outliers was proposed.
 Different constrains related to the telecom industry
(and not only) were mentioned: different aggregation
levels, data problems and industry requirements.
 Several test scenarios were created, to check the
performance of three implemented forecasting (TES,
Periodic DES, and Periodic LinReg) and compare
them.
Thesis summary Outlook

24. Questions?