Consumer price index prediction using Long Short Term Memory (LSTM) based cloud computing

Abstract Infrastructure service model provides different kinds of virtual computing resources such as networking, storage service, and hardware as per user demands. Host load prediction is an important element in cloud computing for improvement in the resource allocation systems. Hosting initialization issues still exist in cloud computing due to this problem hardware resource allocation takes serval minutes of delay in the response process. To solve this issue prediction techniques are used for proper prediction in the cloud data center to dynamically scale the cloud in order for maintaining a high quality of services. Therefore in this paper, we propose a hybrid convolutional neural network long with short-term memory model for host prediction. In the proposed hybrid model, vector auto regression method is firstly used to input the data for analysis which filters the linear interdependencies among the multivariate data. Then the enduring data are computed and entered into the convolutional neural network layer that extracts complex features for each central processing unit and virtual machine usage components after that long short-term memory is used which is suitable for modeling temporal information of irregular trends in time series components. In all process, the main contribution is that we used scaled polynomial constant unit activation function which is most suitable for this kind of model. Due to the higher inconsistency in data center, accurate prediction is important in cloud systems. For this reason in this paper two real-world load traces were used to evaluate the performance. One is the load trace in the Google data center, while the other is in the traditional distributed system. The experiment results show that our proposed method achieves state-of-the-art performance with higher accuracy in both datasets as compared with ARIMA-LSTM, VAR-GRU, VAR-MLP, and CNN models.

Download Full-text

Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v3i3.1086 ◽

2019 ◽

Vol 3 (3) ◽

pp. 357-363

Author(s):

Soffa Zahara ◽

Sugianto ◽

M. Bahril Ilmiddafiq

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Optimization Algorithm ◽

Price Index ◽

Short Term Memory ◽

Wholesale Price ◽

Stochastic Gradient Descent ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Long Short Term Memory (LSTM) is known as optimized Recurrent Neural Network (RNN) architectures that overcome RNN’s lact about maintaining long period of memories. As part of machine learning networks, LSTM also notable as the right choice for time-series prediction. Currently, machine learning is a burning issue in economic world, abundant studies such predicting macroeconomic and microeconomics indicators are emerge. 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 LSTM method. The network model input consists of 28 variables of staple price in Surabaya and the output is CPI value, also the entire development of prediction model are done in Amazon Web Service (AWS) Cloud. In the interest of 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 results indicate that Nadam has 4,008 RMSE’s value, less than other algorithm which indicate the most accurate optimization algorithm to predict CPI value.

Download Full-text

Multilayer Perceptron and Long Short-Term Memory for Predicting Indonesian Composite Stock Price Index Using Macroeconomic Factors

2019 Fourth International Conference on Informatics and Computing (ICIC) ◽

10.1109/icic47613.2019.8985785 ◽

2019 ◽

Author(s):

Muhammad Nizar Yoga Pratama ◽

Ernastuti

Keyword(s):

Multilayer Perceptron ◽

Stock Price ◽

Price Index ◽

Short Term Memory ◽

Short Term ◽

Macroeconomic Factors ◽

Term Memory ◽

Long Short Term Memory ◽

Stock Price Index

Download Full-text

Host load prediction in cloud computing using Long Short-Term Memory Encoder–Decoder

The Journal of Supercomputing ◽

10.1007/s11227-019-02967-7 ◽

2019 ◽

Vol 75 (11) ◽

pp. 7592-7605 ◽

Cited By ~ 6

Author(s):

Hoang Minh Nguyen ◽

Gaurav Kalra ◽

Daeyoung Kim

Keyword(s):

Cloud Computing ◽

Short Term Memory ◽

Load Prediction ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Host Load Prediction ◽

Host Load

Download Full-text

Dynamic Bit Coin Value Prediction

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e7838.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 3702-3707

Keyword(s):

Price Index ◽

Short Term Memory ◽

Neural System ◽

Value Change ◽

Short Term ◽

Term Memory ◽

Value Prediction ◽

Long Short Term Memory

Bitcoin is online money that is utilized worldwide to make online installments. It has thusly become a venture vehicle in itself and is exchanged a route like other open monetary forms. The capacity to foresee the value change of Bitcoin would in this way encourage future venture and installment choices. The objective of this paper is to learn with what exactness the bearing of Bitcoin cost in USD can be anticipated. The value information is sourced from the Bitcoin Price Index. The errand is accomplished with changing degrees of achievement through the usage of a Bayesian streamlined intermittent neural system (RNN) furthermore, a Long Short Term Memory (LSTM) arranges. The LSTM accomplishes the most noteworthy order precision of 59%.

Download Full-text