Bayesovi pristopi ocenjevanja dinamičnih sistemov za potrebe napovedovanja dinamike pretovora pristanišč

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Title:	Bayesovi pristopi ocenjevanja dinamičnih sistemov za potrebe napovedovanja dinamike pretovora pristanišč
Authors:	ID Intihar, Marko (Author) ID Dragan, Dejan (Mentor) More about this mentor... ID Kramberger, Tomaž (Comentor)
Files:	DOK_Intihar_Marko_2019.pdf (3,26 MB) MD5: CBAA9674B8C1C4C8987B2F689AF0F3A0 PID: 20.500.12556/dkum/0e86937a-e31c-489c-956e-d71210555c3a
Language:	Slovenian
Work type:	Dissertation
Typology:	2.08 - Doctoral Dissertation
Organization:	FL - Faculty of Logistic
Abstract:	Napovedovanje še nerealiziranih dogodkov kot je na primer prediktivna analitika povpraševanja po količini blaga oz. storitev, je današnja vsakdanja praksa za večino subjektov industrije. Pristaniška dejavnost tukaj ni izjema, saj je potrebno zagotavljati kvalitetne napovedi bodočega pretovora pristanišč, ki so osnova za uspešno planiranje pristaniških dejavnosti. V doktorski disertaciji je prikazan algoritem, ki združuje izbrano paleto paradigm iz področja statistike in ekonometrije, z namenom zagotavljanja natančnih napovedi bodoče dinamike pristaniškega tovora. Ideja algoritma temelji na modeliranju časovne vrste izhoda ob upoštevanju izbranih vhodov, ki jih sestavljajo ustrezni ekonomski kazalniki. Le ti so predhodnje izbrani s selekcijsko proceduro in predimenzionirani z namenom zmanjševanja računske kompleksnosti in ohranjanja koristnih informacij osnovnih časovnih vrst. Algoritem kombinira MC simulacijo za selekcijo osnovnega nabora kazalnikov, ter izračun dinamičnih faktorskih modelov z uporabo EM algoritma in Kalmanovega filtra. Ti modeli se uporabljajo kot vhodi v ARIMAX modele časovne vrste opazovanega procesa. Celotni mehanizem pa povezuje pet-fazna procedura, ki preigrava različne strukture kandidatov ARIMAX modelov, in na koncu izbere enega kandidata za izbrani pretovor pristanišča. Končni kandidat je robusten in izpolnjuje temeljne statistično-ekonometrične teste, ter je predvsem zmožen zagotavljati zadovoljivo natančne napovedi. Dani algoritem je bil apliciran na realne podatke izbranega pristanišča. Nato smo izvedli komparativno analizo, v kateri dobljene rezultate primerjamo z napovedmi nekaterih standardnih modelov časovnih vrst. Analiza razkriva uporabnost apliciranega algoritma in nakazuje na koristno uporabo v praksi.
Keywords:	Pretovor pristanišč, časovne vrste, prediktivna analitika, MC simulacija, Dinamična faktorska analiza, EM algoritem, Box-Jenkins modeli, makroekonomski indikatorji
Place of publishing:	Ljubljana
Publisher:	[M. Intihar]
Year of publishing:	2018
PID:	20.500.12556/DKUM-70613
UDC:	519.2
COBISS.SI-ID:	512984637
NUK URN:	URN:SI:UM:DK:KBZVFFTR
Publication date in DKUM:	11.04.2019
Views:	2181
Downloads:	217
Metadata:
Categories:	FL
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Licences

License:	CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:	The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:	30.05.2018

Secondary language

Language:	English
Title:	Bayesian methods for estimating parameters of dynamic systems used for port's throughput forecasting
Abstract:	Future events forecasting such as a prediction of demand is nowadays an industry standard. Maritime industry is not an exception since the quality of forecasting of a future port's throughput dynamics is the baseline for port task planning. In our work, an algorithm, which combines several fields of statistics and econometrics, is presented. Algorithm's primary goal is to provide fairly accurate future port's throughput predictions. The idea stands on modeling the output time series concerning the selected inputs. Latter are presented in the form of macroeconomic indicators, which are a priori selected from a bigger set of indicators. For this purpose, an initial data reduction of exogenous indicators has been conducted by regressing different combination of subsets of exogenous indicators randomly chosen in the Monte Carlo procedure, where the optimal set of genuinely influential indicators was chosen by observing the model’s error-based criteria adopted in the multiple regression static procedure. In the next stage, the reduced set of influential indicators is aggregated into dynamic factor models using the EM algorithm and Kalman filter. Derived dynamic factors are used as inputs into the ARIMAX time series models. Complete mechanism is executed based on five-step heuristic procedure, where generating different ARIMAX candidates eventually leads to the selection of the final best candidate. The obtained ARIMAX model is robust, complies with adequate statistical and econometrics tests, and last, but not least it can provide quite accurate forecasts. The algorithm has been applied to the real port's data. Achieved ARIMAX predictions of the throughput data values were compared with the standard benchmarking models’ predictions, whereas the results are promising and reveal a high level of applicability.
Keywords:	Port throughput, time series, predictive analytics, MC simulation, Dynamic factor analysis, EM algorithm, Box-Jenkins models, macroeconomic indicators

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