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Pattern to build a robust trend indicator for automated trading
Published in Noura Metawa, Mohamed Elhoseny, Aboul Ella Hassanien, M. Kabir Hassan, Expert Systems in Finance, 2019
Khalid Abouloula, Ali Ou-Yassine, Salah-ddine Krit
The Bollinger bands index is a measure of the strength of the acceleration of price movements or the measurement of the degree of market instability in a period of time. The objective of this indicator is to find that there are moving and non-stationary levels of support and resistance that help to know the rebound areas in the market movement. Price volatility in the market is whether the market is quiet or there are sharp fluctuations in its movement [14].
Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms
Published in Journal of Intelligent Transportation Systems, 2023
Zulqarnain H. Khattak, Jackeline Rios-Torres, Michael D. Fontaine, Asad J. Khattak
The final input data for CNN consisted of six channels including speed, acceleration, longitudinal jerk in acceleration, longitudinal jerk in deceleration, and Bollinger bands (positive) and Bollinger bands (negative). Speed is estimated from CAN data. The derivative of speed or rate of change of speed over time provides acceleration. Vehicular jerk and Bollinger bands are volatility-based measures used within the input channel to identify SCEs. The volatility measures provide an indication of the variations in instantaneous regimes of driving including acceleration, deceleration (Khattak, Magalotti, et al., 2020; Khattak, Fontaine, Li, et al., 2021; Wang et al., 2015). The volatility concept was utilized since it serves as a surrogate for safety risks, due to representation of erratic vehicular movements in three-dimensions and their availability prior to occurrence of SCEs. While the literature (Khattak, Smith, et al., 2020; Khattak, Fontaine, Li, et al., 2021; Wang et al., 2015) includes coefficient of variation and vehicular jerk based measures of driving volatility, this study develops Bollinger bands as novel measures of driving volatility to be used as kinematic thresholds for SCE detection. Bollinger bands are volatility indicators that are plotted as envelopes at standard deviation levels over a moving average that indicates the trend in volatile behavior. They become wider as standard deviations increase. Coefficient of variation is a simple measure of volatility that shows dispersion in the data, while Bollinger bands provides an efficient way of identifying volatile driving (Lento et al., 2007) over time and are estimated relative to the moving average of a specific measure (such as speeds and accelerations).