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|a Kartelo, Josip
|9 40234
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| 245 |
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|a Predviđanje korisničkih kupovnih navika zasnovano na metodama analize vremenskih serija :
|b diplomski rad /
|c Josip Kartelo ; [mentor Goran Delač].
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| 246 |
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|a Prediction of Consumer Shopping Habits Based On Time Series Analysis
|i Naslov na engleskom:
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| 260 |
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|a Zagreb,
|b J. Kartelo,
|c 2019.
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| 300 |
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|a 61 str. ;
|c 30 cm +
|e CD-ROM
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| 502 |
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|b diplomski studij
|c Fakultet elektrotehnike i računarstva u Zagrebu
|g smjer: Telekomunikacije i informatika, šifra smjera: 53, datum predaje: 2019-06-28, datum završetka: 2019-07-08
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| 520 |
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|a Sažetak na hrvatskom: U ovom radu napravljeno je predviđanje korisničkih kupovnih navika bazirano na metodama analize vremenske serije. Objašnjene su vremenske serije te je dan pregled potrebnih koraka za analizu vremenskih serija. Opisani su korišteni modeli predviđanja: metoda autoregresivnog intervalnog pomičnog prosjeka - ARIMA, metoda autoregresivnog intervalnog pomičnog prosjeka s vanjskim varijablama - SARIMAX, duboka neuronska mreža, model ekstremnog gradijentnog pojačanja - XGBOOST te Facebook Prophet. Pojašnjen je korišteni skup podataka. Detaljno je prikazan korak istraživanja i analize podataka te je vizualno popraćen grafičkim primjerima. Pokazan je proces modeliranja klasičnim tehnikama ARIMA i SARIMAX. Korišten je model duboke neuronske mreže iz biblioteke Tensorflow. Iz biblioteke XGBoost upotrijebljen je istoimeni model. Posljednji korišteni model je Facebook Prophet. Svi korišteni modeli pripadaju bibliotekama otvorenog koda. Korišteni modeli su detaljno opisani i vizualno popraćeni. Napravljena je usporedba rezultata korištenih metoda te je donesen zaključak.
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| 520 |
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|a Sažetak na engleskom: In this thesis, the forecasting of customer shopping habits was done based on time series analysis methods. An overview of time series analysis theory was given with necessary phases included in building and deploying the forecasting model. The predictive models used for time series forecasting are: autoregressive interval moving average - ARIMA, seasonal autoregressive interval moving average with exogenous variables - SARIMAX, deep neural network, extreme gradient boosting - XGBoost and Facebook Prophet. The data sets used for predictions were clarified and explained. Exploratory data analysis is made and is shown in detail, visually accompanied by graphic examples. The modeling process is demonstrated by classical ARIMA and SARIMAX techniques. A deep neural network model from the Tensorflow library was used. Xgboost model was used from the XGBoost library. The last model used in this thesis is Facebook Prophet. All presented models are part of open source libraries. The models are described in detail with graphics and visualization. A methods' results comparison was made and the conclusion was given.
|
| 653 |
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1 |
|a analiza vremenskih serija
|a predviđanje vremenskih serija
|a strojno učenje
|a ARIMA
|a SARIMAX
|a duboka neuronska mreža
|a Facebook Prophet
|a XGBOOST
|a predviđanje potražnje
|a predviđanje korisničkih kupovnih navika
|
| 653 |
|
1 |
|a time series analysis
|a time series forecasting
|a machine learning
|a ARIMA
|a SARIMAX
|a deep neural network
|a Facebook Prophet
|a XGBOOST
|a forecast demand
|a consumer shopping habits prediction
|
| 700 |
1 |
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|a Delač, Goran
|4 ths
|9 40235
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| 942 |
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|c Y
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| 999 |
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|c 50961
|d 50961
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