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    The Grid-Based Spatial ARIMA Model: An Innovation for Short-Term Predictions of Ocean Current Patterns with Big HF Radar Data
    (2020-01-01)
    Pongto, Ratchanont
    ;
    Wiwattanaphon, Nopparat
    ;
    Lekpong, Peerapon
    ;
    Lawawirojwong, Siam
    ;
    Srisonphan, Siwapon
    Marine natural disasters have direct impacts on countries as well as their residents living on and near the coast. Warning and monitoring system can aid in reducing the loss of lives in the event of a disaster. HF (high frequency) radar, an IoT-enabled ocean surface current monitoring system, implementation is one of the first attempts towards achieving this goal. Although HF systems can monitor sea current patterns in terms of speed and direction for each of the pixels of the coverage area, it fails to predict future values, which are essential to many applications such as oil-spill trajectory prediction (using the GNOME suite: General NOAA Operational Modeling Environment), water quality control and management, and optimized sea navigation. In this paper, we propose a model, called the grid-based spatial ARIMA (auto-regressive integrated moving average), to estimate the forecast values. As a result, the full potential of the HF systems can be utilized. The method considers not only observations of POI (point of interest), but also its neighboring pixels when predicting future values. The proposed method is implemented and compared with other existing approaches, including baseline, kNN, traditional ARIMA model, and LSTM (long short-term memory) techniques. The experimental results showed that our approach outperformed other methods in V comp prediction (with RMSEs of 6.23265) with a configuration of (2, 0, 1) as (p, d, q) and a historical dataset of 1 day and 7 h prior. This configuration was found to be the best combination.
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    Predicting SET50 stock prices using CARIMA (Cross Correlation ARIMA)
    (2016-02-08)
    Wichaidit, Sornpon
    ;
    Kittitornkun, Surin
    Investing in stocks is one of the most popular approaches for money investment. This paper aims to predict short-term stock prices of SET50 of Stock Exchange of Thailand (SET). The proposed method is called CARIMA (Cross Correlation Autoregressive Integrated Moving Average. The basic idea of CARIMA is to find the most highly correlated s tock t o predict the target one in addition to ARIMA predicted price. The results of CARIMA model yield better price trends (measured by 10-day correlation coefficient) while % MAEs (Mean Absolute Errors) are quite similar with those of ARIMA.