T his study examines how oil price shocks interact with the stock market index within a nonlinear autoregressive distributed lag model in Iran. Based on quarterly data for the period from 1991 to 2017, the findings revealed statistically significant evidence of short-run and long-run asymmetric behavior of stock market index in response to the positive a...

This paper proposes an electricity demand and price forecast model of the smart city large datasets using a single comprehensive Long Short-Term Memory (LSTM) based on sequence-to-sequence network. Real market data from Australian Energy Market Operator (AEMO) is used to validate effectiveness proposed model. Several simulations with different configurations are executed actual produce reliable...

Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet tra...

<span>This research aimed to create suitable forecasting models with long-short term memory (LSTM) from time series data, the price of rubber smoked sheets (RSS3) using 2,631 data Rubber Authority Thailand for past 10 years. The was divided into two sets: first 2,105 points were used LSTM prediction model; second 526 estimate performance root mean square error (RMSE), absolute percentage ...