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Predicting agricultural commodity prices accurately is of utmost importance due to various factors such as perishability, seasonality, production uncertainty etc. Moreover, the substantial volatility that may be exhibited in time series further adds to the complexity and constitutes a significant challenge. In this paper, a Hidden Markov (HM) guided Deep Learning (DL) models has been developed on nonlinear and nonstationary price data of agricultural commodities for forecasting by considering technical indicators viz., Moving Average (MA), Bollinger Bands (BB), Moving Average Convergence Divergence (MACD), Exponential MA (EMA) and Fast Fourier Transformation (FFT). HM Models (HMMs) can effectively handle the sequential dependencies and hidden states, while DL approach can learn complex patterns and relationships within the price series and thus the drawback of lack of generalization capability in the DL model has been overcome by HMM. In this study, the Potato price data of the Champadanga district of West Bengal, India has been utilized to assess the performance of the proposed technique. HMM has been combined with six baseline DL models viz., Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional LSTM (BiLSTM) and Bidirectional GRU (BiGRU) for forecast modeling. Performance evaluation metrics viz., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and the insightful Diebold–Mariano (DM) test revealed that Hidden Markov hybridized with DL models surpassed baseline DL models in forecasting accuracy for 1-week, 4-week, 8-week and 12-week ahead DL predictions. The proposed approach holds significant promise for enhancing the precision of agricultural commodity price forecasting with far-reaching implications for various stakeholders such as farmers and planners. • A novel Hidden Markov based Deep Learning (DL) models for accurate agricultural commodity price forecasting. • Addresses DL model generalization challenges by integrating sequential dependencies and hidden states obtained from HMMs. • Proposed models outperform baseline models - RNN, CNN, LSTM, GRU, BiLSTM, and BiGRU for price predictions of agriculture commodities. • Evaluated using RMSE, MAPE, MAE and Diebold Mariano test, for accuracy and reliability of the proposed approach. • Offers precision in commodity price forecasts, benefiting stakeholders such as farmers, planners and policymakers. [ABSTRACT FROM AUTHOR]

In this study, a new algorithm that will improve the performance and the solution quality of the ABC (artificial bee colony) algorithm, a swarm intelligence based optimization algorithm is proposed. ABC updates one parameter of the individuals before the fitness evaluation. Bollinger bands is a powerful statistical indicator which is used to predict future stock price trends. By the proposed method an additional update equation for all ABC-based optimization algorithms is developed to speed up the convergence utilizing the statistical power of the Bollinger bands. The proposed algorithm was tested against classical ABC algorithm and recent ABC variants. The results of the proposed method show better performance in comparison with ABC-based algorithm with one parameter update in convergence speed and solution quality. [ABSTRACT FROM AUTHOR]

Designing of scalable routing protocol with prolonged network lifetime for a wireless sensor network (WSN) is a challenging task. WSN consists of large number of power, communication and computational constrained inexpensive nodes. It is difficult to replace or recharge battery of a WSN node when operated in a hostile environment. Cluster based routing is one of the techniques to provide prolonged network lifetime along with scalability. This paper proposes a technique for cluster formation derived from “Grid based method”. We have also proposed a new decentralized cluster head (CH) election method based on Bollinger Bands. Bollinger Bands are based on Upper Bollinger Band and Lower Bollinger Band, both of these bands are extremely reactive to any change in the inputs provided to them. We have used this property of Bollinger Bands to elect CH. Simulation result shows significant improvement in the network lifetime in comparison with other decentralized and ant based algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Bollinger bands are well-known in stock market as a popular technical analysis tool. We found that Black–Scholes stock price model had this Bollinger bands property also. In this paper, we give the proof of this phenomenon, and give a new distribution of a statistics generated by the Bollinger bands. [Copyright &y& Elsevier]

• How are instantaneous driving decisions expressed through volatility related to involvement in safety critical events? • New measures of volatility based on Bollinger bands are introduced. • Volatility represented by Bollinger bands in both lateral and longitudinal direction increases the risk of involvement in unsafe outcome. • Model based on 20 s of time series prior to involvement in unsafe outcomes show high prediction accuracy of 88.1 % and 85.1 %. The availability of large-scale naturalistic driving data provides enormous opportunities for studying relationships between instantaneous driving decisions prior to involvement in safety critical events (SCEs). This study investigates the role of driving instability prior to involvement in SCEs. While past research has studied crash types and their contributing factors, the role of pre-crash behavior in such events has not been explored as extensively. The research demonstrates how measures and analysis of driving volatility can be leading indicators of crashes and contribute to enhancing safety. Highly detailed microscopic data from naturalistic driving are used to provide the analytic framework to rigorously analyze the behavioral dimensions and driving instability that can lead to different types of SCEs such as roadway departures, rear end collisions, and sideswipes. Modeling results reveal a positive association between volatility and involvement in SCEs. Specifically, increases in both lateral and longitudinal volatilities represented by Bollinger bands and vehicular jerk lead to higher likelihoods of involvement in SCEs. Further, driver behavior related factors such as aggressive driving and lane changing also increases the likelihood of involvement in SCEs. Driver distraction, as represented by the duration of secondary tasks, also increases the risk of SCEs. Likewise, traffic flow parameters play a critical role in safety risk. The risk of involvement in SCEs decreases under free flow traffic conditions and increases under unstable traffic flow. Further, the model shows prediction accuracy of 88.1 % and 85.7 % for training and validation data. These results have implications for proactive safety and providing in-vehicle warnings and alerts to prevent the occurrence of such SCEs. [ABSTRACT FROM AUTHOR]

We employ the constituent stocks of Taiwan 50 as our sample and explore if investors can beat the market by trading them as trading signals emitted by Bollinger Bands (BBs). Results reveal that investors might beat the market by taking long positions on stocks as share prices hit the lower BBs, as significantly shown in the positive abnormal returns. In addition, investors might also take the long positions instead of short positions when share prices reach the upper BBs. Thus, investors should employ momentum strategies instead of contrarian strategies while hitting upper BBs. • Exploring the profitability of Bollinger Bands (BBs) for trading the constituent stocks of Taiwan 50. • Revealing that investors might beat the market by buying stocks as share prices hit the lower BBs. • Proposing that contrarian strategies would be appropriate when share prices reach the lower BBs. • Disclosing that investors may beat the market by buying stocks as share prices rise to the upper BBs. • Suggesting that momentum strategies would be proper when share prices hit the upper BBs. [ABSTRACT FROM AUTHOR]