• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.3
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• pp.71-79
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• 2008

Generally the price of agricultural products has much different characteristics from that of manufacturing products. If products have the limitation of long-term storage and the short period of cultivation, the price of products can be more unstable. Moreover, the price forecasting is very difficult because it doesn't follow any cycle or trend. However price can be regarded as risk instead of uncertainty if we can calculate the probability of price. Reliability analysis techniques are used for forecasting the price change of Chinese cabbage. This study aims to show the usability of reliability analysis for price forecasting. A price-forecasting model was developed based on weather data of the first 10 days of the full cultivating cycle (80 days) 70 days and the average price and standard deviation of wholesale market prices from 1996 to 2001 and applied to forecast the boom price, or the orice which is over the tolerance of market prices, of upland Chinese cabbage in 2002 and 2003. Applied results showed the possibility of boom price forecasting using reliability analysis techniques.

• The Journal of Industrial Distribution & Business
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• v.9 no.10
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• pp.83-89
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• 2018

Purpose - The purpose of this study examines when the optimism impact on financial asset price forecasting and the boundary condition of optimism in the financial asset price forecasting. People generally tend to optimistically forecast their future. Optimism is a nature of human beings and optimistic forecasting observed in daily life. But is it always observed in financial asset price forecasting? In this study, two factors were focused on considering whether the optimism that people have applied to predicting future performance of financial investment products (e.g., mutual fund). First, this study examined whether the degree of optimism varied depending on the direction of the prior price trend. Second, this study examined whether the degree of optimism varied according to the forecast period by dividing the future forecasted by people into three time horizon based on forecast period. Research design, data, and methodology - 2 (prior price trend: rising-up trend vs falling-down trend) × 3 (forecast time horizon: short term vs medium term vs long term) experimental design was used. Prior price trend was used between subject and forecast time horizon was used within subject design. 169 undergraduate students participated in the experiment. χ² analysis was used. In this study, prior price trend divided into two types: rising-up trend versus falling-down trend. Forecast time horizon divided into three types: short term (after one month), medium term (after one year), and long term (after five years). Results - Optimistic price forecasting and boundary condition was found. Participants who were exposed to falling-down trend did not make optimistic predictions in the short term, but over time they tended to be more optimistic about the future in the medium term and long term. However, participants who were exposed to rising-up trend were over-optimistic in the short term, but over time, less optimistic in the medium and long term. Optimistic price forecasting was found when participants forecasted in the long term. Exposure to prior price trends (rising-up trend vs falling-down trend) was a boundary condition of optimistic price forecasting. Conclusions - The results indicated that individuals were more likely to be impacted by prior price tends in the short term time horizon, while being optimistic in the long term time horizon.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.271-274
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• 2005

Under competitive power markets, various long-term and short-term contracts based on spot price are used by producers and consumers. So an accurate forecasting for spot price allow market participants to develop bidding strategies in order to maximize their benefit. Artificial Neural Network is a powerful method in forecasting problem. In this paper we used Radial Basis Function(RBF) network to forecast spot price. To learn ANN, in addition to price history, we used some other effective inputs such as load level, fuel price, generation and transmission facilities situation. Results indicate that this forecasting method is accurate and useful.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.185-192
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• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

• Korean Journal of Agricultural Science
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• v.45 no.4
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• pp.859-870
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• 2018

Dried red peppers are a staple agricultural product used in Korean cuisine and as such, are an important aspect of agricultural producers' income. Correctly forecasting both their supply and demand situations and price is very important in terms of the producers' income and consumer price stability. The primary objective of this study was to compare the performance of time series forecasting models for dried red peppers in Korea. In this study, three models (an autoregressive model with exogenous variables [ARX], AR-exponential generalized autoregressive conditional heteroscedasticity [EGARCH], and ARX-EGARCH) are presented for forecasting the wholesale price of dried red peppers. As a result of the analysis, it was shown that the ARX model and ARX-EGARCH model, each of which adopt both the rolling window and the adding approach and use the agricultural cooperatives price as the exogenous variable, showed a better forecasting performance compared to the autoregressive model (AR)-EGARCH model. Based on the estimation methods and results, there was no significant difference in the accuracy of the estimation between the rolling window and adding approach. In the case of dried red peppers, there is limitation in building the price forecasting models with a market-structured approach. In this regard, estimating a forecasting model using only price data and identifying the forecast performance can be expected to complement the current pricing forecast model which relies on market shipments.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• Asia pacific journal of information systems
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• v.11 no.4
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• pp.99-111
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• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.2
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• pp.243-252
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• 2023

Fluctuations in the price of aquaculture products have recently intensified. In particular, wholesale price fluctuations are adversely affecting consumers. Therefore, there is an emerging need for a study on forecasting the wholesale price of aquaculture products. The present study forecasted the wholesale price of olive flounder Paralichthys olivaceus, a representative farmed fish species in Korea, by constructing multivariate long-short term memory (LSTM) and gated recurrent unit (GRU) models. These deep learning models have recently been proven to be effective for forecasting in various fields. A total of 191 monthly data obtained for 17 variables were used to train and test the models. The results showed that the mean average percent error of LSTM and GRU models were 2.19% and 2.68%, respectively.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.65-76
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• 2014

Housing prices are influenced by external shock factors such as real estate policy or economy. Thus, the intervention effect is important for the development of forecasting model for housing price index. In this paper, we examined the degree of effective power of external shock factors for forecasting housing price index and analyzed time series models for efficient forecasting of housing price index. It is shown that intervention models are better than other models in forecasting results using real data based on the accuracy criteria.