20.500.12556/RUL-153482 Napovedovanje inflacije z nevronskimi mrežami z dolgim kratkoročnim spominom Inflation forecasting with long short-term memory neural networks Zaradi velike zaskrbljenosti glede inflacije v razvitih gospodarstvih so natančne napovedi stopenj inflacije bistvenega pomena za dobro informirane odločitve o fiskalni in monetarni politiki. To magistrsko delo analizira uporabo različnih modelov za boljše napovedovanje kratko, srednje in dolgoročnih sprememb inflacije. Opredeljene so tradicionalne metode, kot je model SARIMAX, ter sodobni modeli, ki temeljijo na umetni inteligenci in ekonometriji. Predlagan je model, ki temelji na nevronskih mrežah z dolgim kratkoročnim spominom. Določen je tudi osnovni model AR(1). Vzpostavljen je zanesljiv okvir za ocenjevanje definiranih modelov. Ta vključuje testiranje modelov z uporabo navzkrižnega preverjanja časovnih vrst na podatkih treh različnih gospodarstev. Analiza v vseh modelih prikaže pomanjkanja. Najbolje se osnovnemu modelu približa SARIMAX, medtem ko LSTM izraža potencial pri napovedovanju z večjo količino podatkov. Univerzalno napovedovanje inflacije ostaja nerešeno vprašanje, dokler raziskovalcem ne bodo na voljo obsežni makroekonomski in mikroekonomski podatki z modeli, učinkovitimi v tem okolju. Given the high inflation concerns in advanced economies, accurate forecasts of inflation rates are essential for well-informed fiscal and monetary policy decisions. This master's thesis analyses the use of different inflation forecasting models to better predict short, medium and long term changes. Traditional methods such as the SARIMAX model are identified, as well as modern models based on artificial intelligence and econometrics. A model based on neural networks with long short-term memory is proposed. AR(1) model is defined as a baseline. A reliable framework for the evaluation of defined models is established. It includes model testing using time series cross-validation on data from three different economies. The analysis shows deficiencies in all models. SARIMAX comes closest to the baseline model, while LSTM shows potential in forecasting with a larger amount of data. Universal inflation forecasting remains an open question until researchers have access to comprehensive macroeconomic and microeconomic data with models that work in this environment. umetna inteligenca nevronske mreže inflacija napovedni modeli LSTM artificial intelligence neural networks inflation predictive models LSTM true false false Slovenski jezik Angleški jezik Magistrsko delo/naloga 2024-01-10 08:10:01 2024-01-10 08:10:12 2024-01-22 07:09:27 0000-00-00 00:00:00 2024 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 37068 181837059 Kavcic_Tilen_-_Napovedovanje_inflacije_z_nevronskimi_mrezami_z_dolgim_kratkorocnim_spominom.pdf Kavcic_Tilen_-_Napovedovanje_inflacije_z_nevronskimi_mrezami_z_dolgim_kratkorocnim_spominom.pdf 1 8FACEE77FC5A0D1026C9290BCACBB184 91811c668de3b1ae7424c8c4d9d67c0f844cad7debb12b0560193965d2a64ecc 4cc6ef56-af87-11ee-ba87-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=179499 Fakulteta za računalništvo in informatiko 0 0 0