=Paper=
{{Paper
|id=Vol-2030/HAICTA_2017_paper23
|storemode=property
|title=Estimation and Analysis of Fish Catches by Category Based on Multidimensional Time Series Database on Sea Fishery in Greece
|pdfUrl=https://ceur-ws.org/Vol-2030/HAICTA_2017_paper23.pdf
|volume=Vol-2030
|authors=Georgios Tegos,Kolyo Onkov,Diana Stoyanova
|dblpUrl=https://dblp.org/rec/conf/haicta/TegosOS17
}}
==Estimation and Analysis of Fish Catches by Category Based on Multidimensional Time Series Database on Sea Fishery in Greece==
https://ceur-ws.org/Vol-2030/HAICTA_2017_paper23.pdf
Estimation and Analysis of Fish Catches by Category
Based on Multidimensional Time Series Database on Sea
Fishery in Greece
Georgios Tegos1, Kolyo Onkov2, Diana Stoyanova2
1
Department of Accounting & Finance, Alexander Technological Educational Institute
(A.T.E.I.), Thessaloniki, P.O. Box 141, GR-574, Greece, (e-mail: gtegos@gen.teithe.gr)
2
Department of Mathematics, Computer Science and Physics, Agricultural University, 4000
Plovdiv, Bulgaria, (e-mails: kolonk@au-plovdiv.bg; di_vest@yahoo.com)
Abstract. Multidimensional database on fishery in Greece stores statistical
time series on quantity of fish catches by areas, species, months, kind of
fishery, fishing tools and category as well as by value and employment. The
averages of total fish quantity by category for the period 2004-2015 compared
to 1992-2003 period generally decrease. An exception is the quantity of fish
category I, caught by Trawl Nets (Open Sea) and Ring Nets, where there is
almost balance between the average values of catches. Trend modeling and
exponential means methods are applied as smoothing and forecasting
techniques. The decreasing rate per year on total fish catches for the period
2004-2015 is -3.44%, while the rate by category I, II and III is -2.67%, -2.96%
and -3.71%, respectively. Exponential means is proven to be the proper
method for forecasting the fish catch quantity because there is a big fluctuation
in the time series values.
Keywords: time series database, fish category, trend, exponential means,
forecasting
1 Introduction
Greece supports marine fishery in order to enhance domestic sea food
consumption and increases export to other countries. The fishery sector is still
suffering from overfishing, fleet over-capacity, heavy subsidies, low economic
resilience and decline in the volume and size of fish caught. In Mediterranean
European countries, 85% of the assessed stocks are currently overfished compared to
a maximum sustainable yield reference value (MSY) (Colloca et al, 2011). A
management system that can encourage a spread of fishing effort without penalizing
any one section of the fleet would be welcomed (Thomson, 1984). Taking into
account all these threats sustainability has to be in priority. Sustainable development
meets the needs of the current generation without compromising the ability of future
generations to meet their own needs (OECD, 2002).
The Hellenic Statistical Authority (ELSTAT) announces the results for every year
with a delay of two years. For comparability reasons, data for the two previous years
214
are also made available. It constructs three year-long time series but longer ones are
needed for statistical estimations and analysis. ELSTAT database is a reliable source
of fishery data in Greece. The collected statistical data concern different aspects of
activities regarding fishery sector as: economical, technical, biological, space and
time. Multidimensional FTS (Fishery Time Series) database on fishery in Greece,
created by using statistical data from ELSTAT database, stores 2241 time series on
quantity of fish catches by areas, species, months, kind of fishery, fishing tools and
category as well as by value and employment for the period 1990-2015 (Tegos &
Onkov, 2015).
The aim of this paper is to estimate fish catches by category based on
multidimensional time series database on sea fishery in Greece in order useful
information for fishery, economy and fish resources to be derived.
2 Materials and Methods
Figure 1 presents the three basic statistical units regarding fishery in Greece:
quantity and value of fish catch, and employment. The number of dimensions and
attributes is shown in parentheses. The whole set of data has temporal character. The
relational data model of FTS database is based on the hierarchical principle. This
approach has positive characteristics that can be generalized as follows: a) it
facilitates time series visualization and querying, b) it abstracts spatial and temporal
details in datasets and c) it stores, vertically, numerical data in time series in the
lowest level of hierarchy, so as the yearly updating is made easy.
Quantity and Value of catch are both counted, by Kind of fishery and Fishing
tools and then distinguished into three Categories according to their quality: “first”,
“second” and “third”.
Dimensions/Attributes
Quantity of catch by Value of catch by Employment
Areas (18) Kind of fishery (3) Total employment
Category (3)
Fish species (71) Fishing tools (5) Fully employed
Category (3)
Months (12)
Kind of fishery (3)
Category (3)
Fishing tools (5)
Category (3)
Time series
Fig. 1. Multidimensional data on fishery in Greece
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Fish catch quantities concern natural resources and their exploitation while values
depend on quantities and are reliant on economic indicators: inflation, market
features etc. Therefore, it is logical this study to give emphasis on fish catch quantity.
The following methods are used for the analysis of fish catch by categories:
Computing descriptive statistics is applied on time series for the following two
periods: 1992-2003 and 2004-2015. This distinction will give the opportunity to
compare catch quantity and value by categories.
Calculation of shares of catch quantity and value by categories presents the
dynamics of the rates between first (I), second (II) and third (III) category.
Trend modelling is a usual approach for time series analysis. Trend analysis is
carried out on time series for the period 2004-2015, concerning catch quantity of fish
category I, II, III and total, as well. The polynomial (linear, second and third degree)
and exponential trend models are studied. Trend models adequacy is proved by
applying F-test at α=0.05 level of significance. If linear trend model is adequate, the
yearly increase/decrease of fish catch quantities can be estimated.
Forecasting based on Exponential means method. Exponential means method is
considered to be the proper method for forecasting fish catch quantity because there
is big fluctuation in total values (Tegos, 2005). Whenever the development of a
certain event is studied over a long period of time, it is usual certain changes to occur
that will influence the model parameters. The value of the studied event for a given
period will be determined to a great degree by the development conditions, which
were applied during the recent period and less to the distant one. Then, the model
parameters will be determined by assigning greater weight to the more recent
historical values of the event than the distant ones. This is the basic characteristic of
the Exponential means method. Each time series is smoothed through weighted
moving averages, which contribute to the exponential distribution law by their
weights.
Theoretically the exponential means method lays behind the following recurrent
formula for the exponential mean S tp of the p-degree at the moment t (Velichkova,
1981):
S tp = βS tp −1 + (1 − β ) S tp−1 , 0 < β < 1. (1)
For the explanation of the essence of the exponential means method the equation
(1) is transformed in the first degree and related to the time series values as follows:
n (2)
S t1 = β i .
∑ (1 − β ) y
i =1
t −i
The performance of the exponential means method as a smoothing and forecasting
tool for time series is determined by the correct choice of the value of the β
parameter ( 0 < β < 1 ). When it is close to 1, the main influence refers to the last
members of the historical time series while, when it is close to 0, this influence
weakens in favor of the more distant members of the historical time series. There is
no established method of choosing the optimum value of β (Velichkova, 1981). The
216
values of β are varying in the interval (0, 1), for instance with constant step, and
performing all needed computations the forecasting values will be obtained. The
criterion C is calculated for each value of β to ensure the forecasting values
β
corresponding to the smallest standard error Sy between real and smoothed values of
the historical time series. The algorithm and software of the exponential means
method application was created in (Tegos, 2005) PhD dissertation work and it is used
for the current study. Experts consider that, by this method, the longer the series is,
the more exact the forecasted values are.
3 Results, Analysis and Discussion
There is an important correlation between datasets on Quantity and Value by
Categories:
Tables on Fishing tools and Category Tables on Kind of Fishery and Category
Trawl Nets (Overseas) = Overseas Fishery
⎧Trawl Nets (Open sea) ⎫
⎪
+
⎪ = Open Sea Fishery
⎨ ⎬
⎪Ring Nets ⎪
⎩ ⎭
⎧Seine Nets (Open sea) ⎫
⎪ ⎪ = Inshore Fishery
⎨ + ⎬
⎪Others ⎪
⎩ ⎭
In this paper the results on Fishing tools and Category as well as Totals are mainly
discussed. Special attention is given and the discussion is extended when specific
features appear on Kind of fishery and Category.
For fish category I, caught by Trawl Nets (Open Sea) and Ring Nets is important
to point out that the average catch quantity, for the period 2004- 2015, is greater than
that one for the period 1992-2003. The least decrease in total quantity is observed for
category I fish species (-5.3%) while for category II and III is (-56.4) and (-38.5),
respectively. Average fish catch values during the second time period are greater
than the first one when fishing by Trawl Nets (Open Sea) and Ring Nets. Comparing
the average values of the total quantity for the periods 1992-2003 and 2004-2015, it
is noticed that there is almost a balance between these Values, since the difference is
only -1.6 %. This fact is not surprising taking into account the basic market principle
– supply and demand law. The decrease in quantity by -41.7% in the second time
period indicates that almost half quantity of fish catch is lost but this decrease is not
delivered to fish catch values (-1.6%), confirming so the mentioned law. Of course,
in order to be more accurate the inflation has to be deducted from the last period
values.
217
The shares of fish category I and III in quantity and value increases, while the
share of category II decreases. The share of fish category I in quantity is 13.06% for
the period 2004-2015, while its share (26.56%) in value is double as much. Fish
category I, economically the most important one, during time period 2004-2015 is
mainly caught by two types of fishing tools: Trawl Nets (Open Sea) and Others. It is
worthy to mention that the biggest variations are noticed for fish category II.
Adequacy of linear trend model is proved for catch quantity of fish category I, II,
III and total, as well. The decreasing rate per year on total fish catches for the period
2004-2015 is -3.44%, while the rate by category I, II and III is -2.67%, -2.96% and -
3.71%, respectively. The least decrease of catch quantity concerns fish category I.
These results are in accordance with the tendency of the decreasing quantity of fish
resources in Mediterranean Sea. It is important for the economy that category I fish
species contributes to hold the high values in the fish market but the most essential is
to preserve sustainability for fish resources for the benefit of future generations.
The forecasting results on the Quantity of fish catch category I, by using trend
models (linear and exponential) and by exponential means method are presented in
Figure 2.
Trend models are used for short term forecasting. The attained projections are
simple consequence of the trend line extrapolation. Trend models are not considered
very reliable because values of the studied time series on the fish catch quantity for
the first time period (2004-2010) are characterized by a relatively strong decrease
while for the second one (2011- 2015) they are almost stable.
This study empirically shows that for forecasting purposes exponential means
method is more suitable and reliable than trend models. The forecast obtained by this
method is reasonable and acceptable. The forecasting results regarding catch quantity
of fish category II and III are similar.
Fig. 2. Forecasting results of catch quantity category I, based on trend models and exponential
means method
218
4 Conclusion
This study is based on multidimensional time series database on sea fishery in
Greece. The decreasing rate per year on total fish catches by category for the period
2004-2015 is in accordance with the decreasing tendency of fish resources quantity
in Mediterranean. The least decrease is attained on quantity of fish category I.
Despite the encouraging outcome in category I, the fishing effort has to be spread to
all fish species and not directed only to this category because there is the risk of
depletion of the related fish resources. Exponential means is proven to be the proper
method for forecasting the fish catch quantity because there is a big fluctuation in
time series values.
Analytical information obtained in this study can be useful for taking decisions in
the use of fishing tools and in fishery resources management.
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219