Time series prediction of rain rate during rain events at a tropical location

<p>Commercial microwave links (CMLs) can be used for quantitative precipitation estimation. The measurement technique is based on the exploitation of the close to linear relationship between the attenuation of the signal level by rainfall and the path averaged rain rate. At a temporal resolution of one minute, the signal level of almost 4000 CMLs distributed all over Germany is being recorded since August 2017, resulting in one of the biggest CML data sets available for scientific purposes. A crucial step for retrieving rainfall information from this large CML data set is to accurately detect rainy periods in the time-series, a process which is hampered by strong signal fluctuations, occasionally occurring even when there is no rain. In our study, we evaluate the performance of convolutional neural networks (CNNs) to distinguish between rainy and non-rainy signal fluctuations by recognizing their specific patterns. CNNs make use of many layers and local connections of neurons to recognize patterns independent of their location in the time-series. We designed a custom CNN architecture consisting of a feature extraction and classification part with 20 layers of neurons and 1.4&#8201;x&#8201;10<sup>5</sup> trainable parameters. To train the model and validate the results we refer to the gauge-adjusted radar product RADOLAN-RW, provided by the German meteorological service. Despite not being an absolute truth, it provides robust information about rain events at the CML locations at an hourly time resolution. With only 400 CMLs used for training and 3504 for validation, we find that CNNs can learn to recognize different signal fluctuation patterns and generalize well to sensors and time periods not used for training. Overall we find a good agreement between the CML and weather radar derived rainfall information by detecting on average 87&#8201;% of all rainy and 91&#8201;% of all non-rainy periods.</p>

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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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