Using MLP and RBF Neural Networks to Improve thePrediction of Exchange Rate Time Series with ARIMA

Kia, Arash Negahdari

doi:10.7763/ijiee.2012.v2.157

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…In this paper, a multilayer perceptron neural network was used that consists of 3 layers with 100 neurons in each layer, and the output of each layer is given to the next layer as input. Multilayer perceptron neural networks are nonlinear models with one or multiple inputs, along with hidden layers that connect these inputs to one or more outputs in a nonlinear manner [24]. As mentioned earlier, this model is designed to classify text into two categories, whether it is generated by AI or written by humans.…”

Section: Methodsmentioning

confidence: 99%

A Framework for Detecting AI-Generated Text in Research Publications

Sarzaeim,

Doshi,

Mahmoud

2023

icat

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The use of generative artificial intelligence is becoming increasingly prevalent in creating content in various formats such as text, video, and image. However, there is a need to distinguish between content that has been generated by humans and content that has been generated by AI as misuse of these technologies can raise scientific and social challenges. Moreover, there are concerns about the reliability and comprehensiveness of the content generated by AI without human validation. This paper presents a framework for AI-generated text. The prototype implementation of the proposed approach is to train a model using predefined datasets and deploy this model on a cloud-based service to predict whether a text was created by a human or AI. This approach is specifically focused on assessing the accuracy of scientific writings and research papers rather than general text. The proposed framework is compared with recently developed tools such as OpenAI Text Classifier, ZeroGPT, and Turnitin. The results show that training a text classifier can be highly useful in detecting whether a text is written by a human or AI. The source code and dataset are made open source so others can experiment with the prototype implementation and use it for future research.

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Section: Methodsmentioning

confidence: 99%

A Framework for Detecting AI-Generated Text in Research Publications

Sarzaeim,

Doshi,

Mahmoud

2023

icat

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“…ARIMA, the most well-known statistical method for time series analyses, can model complex patterns and forecast in univariate time series data [43]. The ARIMA model function has three important factors, denoted as p, d, and q [44,45], which represent the autoregressive, integration, and moving average factors, respectively. An expression of the ARIMA (p, d, q) model in general is as follows [44]:…”

Section: Autoregressive Integrated Moving Average (Arima)mentioning

confidence: 99%

A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

Son

Kim

2020

Sustainability

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Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.

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“…The application of various methods can complement each other and produce a weighty result for the time series prediction [11]. The manner of combine different models is generally additive; this feature can influence the performance of forecasting [5,7,13].…”

Section: Introductionmentioning

confidence: 99%

Hybrid wavelet model for time series prediction

Pereira¹,

Scarpin²,

Teixeira³

2015

ams

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To improve time series forecasts the wavelet decomposition has been applied. The combination of forecasting methods as the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks have been used to achieve a higher quality time series forecasting than. This paper proposed a hybrid model composed of wavelet decomposition, ARIMA and neural network Multilayer Perceptron. These models are combined linearly then yielding the time series forecasting. The series studied are the Wolf's sunspots and the British pound/US dollar exchange rate data. The comparison of the proposed model in this paper with literature indicated an effective way to improve forecasting. 7432Eliete Nascimento Pereira et al.

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Using MLP and RBF Neural Networks to Improve thePrediction of Exchange Rate Time Series with ARIMA

Cited by 6 publications

References 10 publications

A Framework for Detecting AI-Generated Text in Research Publications

A Framework for Detecting AI-Generated Text in Research Publications

A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

Hybrid wavelet model for time series prediction

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