Navigating Oxford’s Forex And Mining Scene: Tips For Success


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Navigating Oxford’s Forex And Mining Scene: Tips For Success

Navigating Oxford's Forex And Mining Scene: Tips For Success

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By Yaxi Su Yaxi Su Scilit Google Scholar 1 , Chaoran Cui Chaoran Cui Scilit Google Scholar 1, * and Hao Qu Hao Qu Scilit Google Scholar 2

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School of Computer Science and Technology, Shandong University of Finance and Economics, no. 7366, East Second Ring Road, Yaojia Sub-District, Jinan 250014, China

Received: March 2, 2022 / Revised: March 24, 2022 / Accepted: March 30, 2022 / Published: April 1, 2022

Time series prediction has been studied for decades because of its potential in a wide variety of applications. As one of the most popular technical indicators, the moving average summarizes the overall changing patterns over the past period and is often used to predict the future trend of time series. However, traditional moving average indicators are calculated by averaging time series data with equal or predefined weights and ignore the subtle difference in the importance of different time steps. In addition, unchanged data weights will be applied to different time series regardless of differences in their underlying characteristics. Furthermore, using moving averages of different scales to predict future trends ignores the interaction between different dimensions of different indicators. In this paper, we propose a learning-based moving average indicator called self-aware moving average (SAMA). After encoding the input time series signals based on recurrent neural networks, we introduce a self-attention mechanism to adaptively determine the weights of the data at different time steps to calculate the moving average. In addition, we use multiple self-attention heads to model SAMA indicators of different scales and finally combine them through a bilinear fusion network for time series prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of our approach. The data and codes of our work have been published.

Navigating Oxford's Forex And Mining Scene: Tips For Success

Time series analysis has been applied in a wide range of practical problems, such as financial market prediction [1], electricity load forecasting [2], as well as weather and environmental forecasting [3, 4, 5, 6]. As it is relatively difficult to estimate the exact values ​​of time series, recent studies have mostly paid attention to assessing the trend of time series in the future. Time series prediction can usually be thought of as a classification problem in which the goal is to predict the future direction of time series movement, such as rising, falling, and stable trends.

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As one of the most popular technical indicators in time series analysis, moving average indicators can easily and quickly summarize the overall changing patterns of time series over the last period [7]. Although traditional moving average indicators are commonly used in various applications, they are calculated by averaging data from different time steps with equal or predefined weights. However, the same weighting scheme cannot reflect the difference in the importance of different time steps, while manually determining the weights requires a considerable amount of domain knowledge and engineering skills. In addition, unchanged data weights will be applied to different time series regardless of differences in their underlying characteristics.

In general, we can perform time series prediction by comparing moving averages at different scales. For example, in the stock market, the short-term moving average crosses above or falls below the long-term moving average, which are usually considered bullish and bearish signals [8]. However, such strategies are still heuristic rules that ignore the interaction effect between the dimensions of moving averages of different scales, and it remains an open question how to better use moving averages from multiple scales for time series prediction.

To solve the above problems, in this paper, we propose a learning-based moving average indicator, called self-aware moving average (

). Specifically, after encoding the input time series signals based on recurrent neural networks (RNNs), we introduce a self-observation mechanism [9] to adaptively determine the weight of the data at each time step to calculate moving averages. In addition, we use multiple self-aware heads to model moving average indicators of various scales. The bilinear model takes into account the interaction between the moving average dimensions of different scales and provides a richer representation than the linear model. Numerous studies [10, 11, 12] have demonstrated its effectiveness in various fields, and we proposed a bilinear fusion subnet to integrate them for efficient time series prediction in an end-to-end manner. Extensive experiments on two real-world datasets confirm the rationality of combining multiple scales

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Indicators and show that our method significantly outperforms traditional moving average indicators as well as modern sequential modeling methods for time series prediction. The data and codes of our work are available at (accessed 28 February 2022).

The remainder of the paper is organized as follows. Section 2 reviews related work. Section 3 presents some methodological basics regarding moving average indicators. Section 4 details the proposed framework for time series prediction. The experimental setup is described in Section 5. Section 6 concludes our work.

Previously, statistical models such as autoregressive (AR) [13], moving average (MA) [14] and autoregressive moving average [15] and its variant ARIMA [16] were widely used for time series prediction. However, these models cannot describe non-linear changes in time series. To solve this problem, researchers have resorted to some nonlinear models such as kernel method [17], Gaussian process [18] and hidden Markov model [19], support vector machines [20] and least squares support vector machine [21], which they have the ability to adapt to complex time series. Experiments show that they achieve good prediction efficiency and accuracy in short-term prediction.

Navigating Oxford's Forex And Mining Scene: Tips For Success

With the rapid development of deep learning, CNN [22], RNN [23, 24] and their variants, including long-short-term memory (LSTM) [4] and gate recurrent unit (GRU) [25], have attracted much attention and become popular methods time series modeling. For example, Qin et al. [26] proposed a two-stage attention-based recurrent neural network (DA-RNN) that includes an encoder and a decoder. The former with an input attention mechanism adaptively extracts the input features of each time step, and the latter with a temporal attention mechanism selects the relevant hidden states of the encoder among all time steps in different phases. In recent years, the transformer architecture [27] has achieved great success in time series prediction. It relies entirely on the self-observation mechanism for time series modeling and is reported to achieve promising results.

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The self-observation mechanism has been widely used in time series prediction. For example, the constant outage of high-voltage transmission lines increases line instability and presents varying degrees of hidden safety hazards that increase line loads and line repair costs. In response to the above problems, Fahim et al. [28] proposed a self-attention convolutional neural network (SAT-CNN) model based on time series imaging for feature extraction and a combined self-notification mechanism with CNN to make the model more accurately identify specific fault types for accurate classification. Finally, the effectiveness of the proposed SAT-CNN model is tested with the number of combined input voltage, current and voltage and current signals at different sampling rates. Jin et al. [29] applied the self-observation mechanism to the field of agriculture and proposed a two-way self-observation encoding and decoding (BEDA) framework. First, a wavelet threshold filter and preprocessing are used to denoise the time series, and then a bidirectional network with long short-term memory is used to extract the features of the time series. Then the multi-attention mechanism is introduced into the encoder-decoder framework. Finally, factors of the internal environment (temperature, humidity and CO

) are accurately predicted to provide good conditions for crop cultivation and growth. Experiments show that the framework has good robustness and generalizability. With the development of modern intelligent transport systems (ITS), we are effectively gaining

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