Time series prediction using kernel adaptive filter with least mean absolute third loss function

Purpose The purpose of this paper is to propose an optimal predictive model for the short-term forecast of real-time non-stationary machine variables by combining time series prediction with adaptive algorithms to minimize the error and to improve the prediction accuracy. Design/methodology/approach The proposed model is applied for prediction of speed and controller set point of three-phase induction motor operating on closed loop speed control with AC drive and PI controller. At Stage 1, the trend of the machine variables has been extracted and added to auto-regressive moving average (ARMA) time series prediction. ARMA prediction has been carried out using different combinations of AR and MA methods in order to make prediction with less Mean Squared Error (MSE). Findings The prediction error indicates the inadequacy of the model to estimate the data characteristics, which has been resolved at the subsequent stage by cascading an adaptive least mean square finite impulse response filter to the time series model. The adaptive filter receives the predicted output including training data and iteratively adjusts its coefficients for zero error convergence. Research limitations/implications The componentized data prediction based on time series and cascade adaptive filter algorithm decomposes the non-stationary data characteristics for predictive maintenance. Evaluation of the model with different combination of time series algorithms and parameter settings of adaptive filter has been carried out to illustrate the performance of the prediction model. This prediction accuracy is compared with existing linear adaptive filter prediction using MSE as comparison index. The wide margin in the MSE values substantiates the prediction efficiency of the proposed model for machine data. Originality/value This model predicts the dynamic machine data with component decomposition at high accuracy, which enables to interpret the system response under dynamic conditions efficiently.

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Levenberg-Marquardt Algorithm for Mackey-Glass Chaotic Time Series Prediction

Discrete Dynamics in Nature and Society ◽

10.1155/2014/193758 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 6

Author(s):

Junsheng Zhao ◽

Yongmin Li ◽

Xingjiang Yu ◽

Xingfang Zhang

Keyword(s):

Time Series ◽

Loss Function ◽

Multilayer Perceptron ◽

Time Series Prediction ◽

Chaotic Time Series ◽

Convergence Speed ◽

Gradient Descent Method ◽

Marquardt Algorithm ◽

Levenberg Marquardt ◽

Chaotic Time Series Prediction

For decades, Mackey-Glass chaotic time series prediction has attracted more and more attention. When the multilayer perceptron is used to predict the Mackey-Glass chaotic time series, what we should do is to minimize the loss function. As is well known, the convergence speed of the loss function is rapid in the beginning of the learning process, while the convergence speed is very slow when the parameter is near to the minimum point. In order to overcome these problems, we introduce the Levenberg-Marquardt algorithm (LMA). Firstly, a rough introduction is given to the multilayer perceptron, including the structure and the model approximation method. Secondly, we introduce the LMA and discuss how to implement the LMA. Lastly, an illustrative example is carried out to show the prediction efficiency of the LMA. Simulations show that the LMA can give more accurate prediction than the gradient descent method.

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Neural Network Techniques for Time Series Prediction: A Review

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.3.3.281 ◽

2019 ◽

Vol 3 (3) ◽

Author(s):

Muhammad Faheem Mushtaq ◽

Urooj Akram ◽

Muhammad Aamir ◽

Haseeb Ali ◽

Muhammad Zulqarnain

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Time Series Data ◽

Weather Prediction ◽

Time Series Prediction ◽

Series Data ◽

Prediction Problem ◽

Neural Network Models ◽

Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series. Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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Temperature time series prediction based on autoregressive integrated moving average model

Instrumentation Mesure Métrologie ◽

10.3166/i2m.17.443-453 ◽

2018 ◽

Vol 18 (3) ◽

pp. 443-453

Author(s):

Huanhuan ZHENG ◽

Yuxiu BAI ◽

Yaqiong ZHANG

Keyword(s):

Time Series ◽

Moving Average ◽

Time Series Prediction ◽

Temperature Time Series ◽

Average Model ◽

Autoregressive Integrated Moving Average ◽

Moving Average Model ◽

Temperature Time

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Making investment decisions in stock markets using a forecasting-Markowitz based decision-making approaches

Journal of Modelling in Management ◽

10.1108/jm2-12-2018-0217 ◽

2019 ◽

Vol 15 (2) ◽

pp. 647-659 ◽

Cited By ~ 1

Author(s):

Zahra Moeini Najafabadi ◽

Mehdi Bijari ◽

Mehdi Khashei

Keyword(s):

Decision Making ◽

Time Series ◽

Stock Markets ◽

Time Series Prediction ◽

Investment Decisions ◽

Rate Of Return ◽

Expected Returns ◽

Prediction Methods ◽

Content Type ◽

Markowitz Model

Purpose This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches. Design/methodology/approach The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the expected rate of return based on distribution. Findings The results show that using time series prediction methods has a significant effect on improving investment decisions and the performance of the investments. Originality/value In this study, in contrast to previous studies, the alteration in the Markowitz model started with the investment expected rate of return. For this purpose, instead of considering the distribution of returns and determining the expected returns, time series prediction methods were used to calculate the future return of each asset. Then, the results of different time series methods replaced the expected returns in the Markowitz model. Finally, the overall performance of the method, as well as the performance of each of the prediction methods used, was examined in relation to nine stock market indices.

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A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽

10.3390/en14010141 ◽

2020 ◽

Vol 14 (1) ◽

pp. 141

Author(s):

Jacob Hale ◽

Suzanna Long

Keyword(s):

Time Series ◽

Electricity Generation ◽

Sustainability Assessment ◽

Moving Average ◽

Time Series Prediction ◽

State Level ◽

Autoregressive Integrated Moving Average ◽

Energy Transitions ◽

Univariate Time Series ◽

Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

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