Open AccessConsumer price index prediction using Long Short Term Memory (LSTM) based cloud computing
Author(s) -
Soffa Zahara,
Sugianto Sugianto,
Muhammad Bahril Ilmiddaviq
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1456/1/012022
Subject(s) - inflation (cosmology) , computer science , consumer price index (south africa) , index (typography) , term (time) , gradient descent , artificial neural network , econometrics , stochastic gradient descent , recurrent neural network , value (mathematics) , long short term memory , artificial intelligence , machine learning , monetary policy , economics , physics , quantum mechanics , world wide web , theoretical physics , monetary economics
Long Short Term Memory (LSTM) is known as optimized Recurrent Neural Network (RNN) architectures that overcome the lack of RNN’s about maintaining long period of memories information. As part of machine learning networks, LSTM also notable as the right choice for time-series prediction. Inflation rate has been used for decision making for central banks also private sector. In Indonesia, CPI (Consumer Price Index) is one of best practice inflation indicators besides Wholesale Price Index and The Gross Domestic Product (GDP). Since CPI data could be used as a direction for next inflation move, we conducted CPI prediction model using Long Short Term Memory Method. The network model input consists of 34 variables of staple price in Surabaya and the output is CPI value. In the interest of predictive accuracy improvement, we used several optimization algorithm i.e. Stochastic Gradient Descent (sgd), Root Mean Square Propagation (RMSProp), Adaptive Gradient (AdaGrad), Adaptive moment (Adam), Adadelta, Nesterov Adam (Nadam) and Adamax. The result indicate that Nesterov Adam has 4.088 RMSE’s value, less than other algorithm which indicate the most accurate optimization algorithm to predict CPI value.