Forecasting the short-term trend of a stock market has long been a big challenging task. Parameters of stock markets, including open/close prices, daily-high/low prices and trading volumes, were frequently used in previous studies to forecast the stock market. Basing on the fact that the moving direction of these parameters have certain inertia within short-term period, we here explored the pot...

Since its founding in 2008, Bitcoin (financial code: BTC) has emerged as a digital currency market cap and continues to attract investors policymakers' attention. In recent years, BTC high price volatility, substantial increase 2016, followed by significant decline 2018. Unlike stock markets, is open for 24x7 dan no closing period. It means everyone can trade it any time. However, this flexibil...

Developing forecasting models for estimating the behavior of capital markets is one of the most challenging tasks in financial decision support system research. Besides time series models, artificial neural network approaches and genetic algorithms, text mining technologies represent a promising approach to support financial decision-making. In this paper, the authors address the problem field ...

In this job, short-term forecasts are calculated for the Energy Price in the Electricity Production Market of Spain. The methodology used to achieve these forecasts is based on Artificial Neural Networks, which have been used succesfully in recent years in many forecasting applications. To gauge the quality of forecasts, they have been compared with those obtained with the Box-Jenkins ARIMA mod...

abstract forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. this paper studies load consumption modeling in hamedan city province distribution network by applying esn neural network. weather forecasting data such as minimum day temperature, average day temp...

We outline initial concepts for an immune inspired algorithm to evaluate and predict oil price time series data. The proposed solution evolves a short term pool of trackers dynamically, with each member attempting to map trends and anticipate future price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. The resulting sequence ...

In electricity markets, locational marginal price (LMP) forecasting is particularly important for market participants in making reasonable bidding strategies, managing potential trading risks, and supporting efficient system planning operation. Unlike existing methods that only consider LMPs' temporal features, this paper tailors a spectral graph convolutional network (GCN) to greatly improve t...

It is widely acknowledged that electricity price forecasting become an essential factor in operational activities, planning, and scheduling for the participant price-setting market, nowadays. Nevertheless, became a complex signal due to its non-stationary, non-linearity, time-variant behavior. Consequently, variety of artificial intelligence techniques are proposed provide efficient method shor...