20.500.12556/RUL-130172 Primerjava metode Gradient boosting in nevronskih mrež z Uberjevo arhitekturo za napovedovanje prodaje Comparison between Gradient Boosting and Neural Networks with Uber Architecture for Sales Forecasting Diplomska naloga govori o razlikah med metodo Gradient boostinga z regresijskimi drevesi (GBRT) in Uberjevimi nevronskimi mrežami pri napovedovanju prodaje. Napovedovanje prodaje je pomembno zaradi vedno večjega števila izdelkov in omejenega prostora v skladiščih. Obstaja že dolga zgodovina napovedovanja prodaje, ki se razdeli na več dob. Najnovejša med njimi je doba napovedovanja s strojnim učenjem, kamor spadata tudi obravnavani metodi diplomske naloge. Metode so bile testirane na podatkih tekmovanja iz spletne strani Kaggle, kjer je bil cilj tekmovanja napovedati prodajo za 10 različnih Wallmartovih trgovin za obdobje 28 dni.Na splošno je bil bolj uspešen model, ki je uporabljal metodo GBRT, ki se je izkazala bolje na volatilnem in stabilnem obdobju in napovedala bolje, kot Uberjeva arhitektura LSTM (ULSTM). ULSTM se je izkazala bolje v krajših časovnih obdobjih in v enem, kljub splošni prevladi metode GBRT napovedala bolje. Pri večjih naborih atributov se je metoda GBRT izkazala za veliko hitrejšo od ULSTM, prav tako je bila velika razlila pri vplivu naključnega semena na rezultate, kar se je izkazalo za večji problem pri ULSTM. Rezultati testiranja so pokazali, da obstaja veliko razlogov zakaj je Gradient boosting veliko bolj uporabljen pri napovedovanju časovnih serij, kot LSTM. The diploma thesis talks about the differences between Gradient boosting with regression trees (GBRT) and Uber neural networks in sales forecasting. Sales forecasting is important because of the ever-increasing number of products and limited space in warehouses. There is a long history of sales forecasts, which is divided into several periods. The latest among them is the period of forecasting through machine learning, which also includes the methods discussed methods. The methods were tested on data from a competition on Kaggle, where the goal of the competition was to predict sales for 10 Wallmart stores for a period of 28 days. In general, the more a successful method was GBRT method, which overall predicted better than Uber’s LSTM architecture (ULSTM). ULSTM performed better in shorter time periods and in one, despite the general dominance of the GBRT method, predicted better. For larger sets of attributes, the GBRT method proved to be much faster than ULSTM and there was also a large difference between the impact of random seed on the results, which proved to be a major problem with ULSTM. Test results showed that there are many reasons why Gradient boosting is much more used in forecasting time series such as LSTM. LSTM Gradient boosting napoved prodaje Uber LSTM Gradient boosting sales forecasting Uber true false false Slovenski jezik Angleški jezik Diplomsko delo/naloga 2021-09-10 18:00:06 2021-09-10 18:00:13 2022-09-09 04:15:08 0000-00-00 00:00:00 2021 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 31063 77313283 Debelak_Jan_-_Primerjava_metode_Gradient_boosting_in_nevronskih_mrez_z_Uberjevo_arhitekturo_za_n.pdf Debelak_Jan_-_Primerjava_metode_Gradient_boosting_in_nevronskih_mrez_z_Uberjevo_arhitekturo_za_n.pdf 1 E72942519BB618BDA60B8F3D363BC3AD 11ddbfbda472ab7ad0c5f6aaed491bc48dea5651c6ce022546448ce05beadba3 eecd38d0-124f-11ec-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=147234 Fakulteta za računalništvo in informatiko 0 0 0