scholarly journals Demand forecasting: an alternative approach based on technical indicator Pbands

2021 ◽  
Vol 12 (4) ◽  
pp. 1063-1094
Author(s):  
Andrea Kolková ◽  
Aleksandr Ključnikov
Keyword(s):  
Neural Networks ◽  
Statistical Methods ◽  
Hybrid Model ◽  
Demand Forecasting ◽  
Google Trends ◽  
Short Term ◽  
Technical Indicators ◽  
Demand Prediction ◽  
Alternative Approach ◽  
Technical Indicator

Research background: Demand forecasting helps companies to anticipate purchases and plan the delivery or production. In order to face this complex problem, many statistical methods, artificial intelligence-based methods, and hybrid methods are currently being developed. However, all these methods have similar problematic issues, including the complexity, long computing time, and the need for high computing performance of the IT infrastructure. Purpose of the article: This study aims to verify and evaluate the possibility of using Google Trends data for poetry book demand forecasting and compare the results of the application of the statistical methods, neural networks, and a hybrid model versus the alternative possibility of using technical analysis methods to achieve immediate and accessible forecasting. Specifically, it aims to verify the possibility of immediate demand forecasting based on an alternative approach using Pbands technical indicator for poetry books in the European Quartet countries. Methods: The study performs the demand forecasting based on the technical analysis of the Google Trends data search in case of the keyword poetry in the European Quartet countries by several statistical methods, including the commonly used ETS statistical methods, ARIMA method, ARFIMA method, BATS method based on the combination of the Cox-Box transformation model and ARMA, artificial neural networks, the Theta model, a hybrid model, and an alternative approach of forecasting using Pbands indicator.  The study uses MAPE and RMSE approaches to measure the accuracy. Findings & value added: Although most currently available demand prediction models are either slow or complex, the entrepreneurial practice requires fast, simple, and accurate ones. The study results show that the alternative Pbands approach is easily applicable and can predict short-term demand changes. Due to its simplicity, the Pbands method is suitable and convenient to monitor short-term data describing the demand. Demand prediction methods based on technical indicators represent a new approach for demand forecasting. The application of these technical indicators could be a further forecasting models research direction. The future of theoretical research in forecasting should be devoted mainly to simplifying and speeding up. Creating an automated model based on primary data parameters and easily interpretable results is a challenge for further research.

Taxi Booking Mobile App Order Demand Prediction Based on Short-Term Traffic Forecasting

2017 ◽  
Vol 2634 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Yunxuan Li ◽  
Jian Lu ◽  
Lin Zhang ◽  
Yi Zhao
Keyword(s):  
High Frequency ◽  
Demand Forecasting ◽  
Low Frequency ◽  
Original Data ◽  
Mobile App ◽  
Support Vector ◽  
Short Term ◽  
Traffic Demand ◽  
Traffic Forecasting ◽  
Demand Prediction

The Didi Dache app is China’s biggest taxi booking mobile app and is popular in cities. Unsurprisingly, short-term traffic demand forecasting is critical to enabling Didi Dache to maximize use by drivers and ensure that riders can always find a car whenever and wherever they may need a ride. In this paper, a short-term traffic demand forecasting model, Wave SVM, is proposed. It combines the complementary advantages of Daubechies5 wavelets analysis and least squares support vector machine (LS-SVM) models while it overcomes their respective shortcomings. This method includes four stages: in the first stage, original data are preprocessed; in the second stage, these data are decomposed into high-frequency and low-frequency series by wavelet; in the third stage, the prediction stage, the LS-SVM method is applied to train and predict the corresponding high-frequency and low-frequency series; in the last stage, the diverse predicted sequences are reconstructed by wavelet. The real taxi-hailing orders data are applied to evaluate the model’s performance and practicality, and the results are encouraging. The Wave SVM model, compared with the prediction error of state-of-the-art models, not only has the best prediction performance but also appears to be the most capable of capturing the nonstationary characteristics of the short-term traffic dynamic systems.

The significance of relevance trees in the identification of artificial neural networks input vectors

2013 ◽  
Vol 24 (1) ◽  
pp. 27-34
Author(s):  
G. Manuel ◽  
J.H.C. Pretorius
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Methods ◽  
Demand Forecasting ◽  
Forecast Model ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Wide Range ◽  
Accurate Forecast ◽  
Artificial Neural

In the 1980s a renewed interest in artificial neural networks (ANN) has led to a wide range of applications which included demand forecasting. ANN demand forecasting algorithms were found to be preferable over parametric or also referred to as statistical based techniques. For an ANN demand forecasting algorithm, the demand may be stochastic or deterministic, linear or nonlinear. Comparative studies conducted on the two broad streams of demand forecasting methodologies, namely artificial intelligence methods and statistical methods has revealed that AI methods tend to hide the complexities of correlation analysis. In parametric methods, correlation is found by means of sometimes difficult and rigorous mathematics. Most statistical methods extract and correlate various demand elements which are usually broadly classed into weather and non-weather variables. Several models account for noise and random factors and suggest optimization techniques specific to certain model parameters. However, for an ANN algorithm, the identification of input and output vectors is critical. Predicting the future demand is conducted by observing previous demand values and how underlying factors influence the overall demand. Trend analyses are conducted on these influential variables and a medium and long term forecast model is derived. In order to perform an accurate forecast, the changes in the demand have to be defined in terms of how these input vectors correlate to the final demand. The elements of the input vectors have to be identifiable and quantifiable. This paper proposes a method known as relevance trees to identify critical elements of the input vector. The case study is of a rapid railway operator, namely the Gautrain.

scholarly journals Deep Learning Approach to Power Demand Forecasting in Polish Power System

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6154
Author(s):  
Tomasz Ciechulski ◽  
Stanisław Osowski
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Power System ◽  
Radial Basis Function Network ◽  
Demand Forecasting ◽  
Support Vector ◽  
Learning Approach ◽  
Power Demand ◽  
Diagnostic Features ◽  
Demand Prediction

The paper presents a new approach to predicting the 24-h electricity power demand in the Polish Power System (PPS, or Krajowy System Elektroenergetyczny—KSE) using the deep learning approach. The prediction system uses a deep multilayer autoencoder to generate diagnostic features and an ensemble of two neural networks: multilayer perceptron and radial basis function network and support vector machine in regression model, for final 24-h forecast one-week advance. The period of the data that is the subject of the experiments is 2014–2019, which has been divided into two parts: Learning data (2014–2018), and test data (2019). The numerical experiments have shown the advantage of deep learning over classical approaches of neural networks for the problem of power demand prediction.

Application of deep learning methods for urban water demand forecast modelling

2021 ◽  
Author(s):  
Anjana G Rajakumar ◽  
Avi Anthony ◽  
Vinoth Kumar
Keyword(s):  
Deep Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Forecast Model ◽  
Demand Forecast ◽  
Short Term ◽  
Learning Methods ◽  
Demand Prediction ◽  
Learning Framework ◽  
Water Demand Forecast

<p>Water demand predictions forms an integral part of sustainable management practices for water supply systems. Demand prediction models aides in water system maintenance, expansions, daily operational planning and in the development of an efficient decision support system based on predictive analytics. In recent years, it has also found wide application in real-time control and operation of water systems as well. However, short term water demand forecasting is a challenging problem owing to the frequent variations present in the urban water demand patterns. There are numerous methods available in literature that deals with water demand forecasting. These methods can be roughly classified into statistical and machine learning methods. The application of deep learning methods for forecasting water demands is an upcoming research area that has found immense traction due to its ability to provide accurate and scalable models. But there are only a few works which compare and review these methods when applied to a water demand dataset. Hence, the main objective of this work is the application of different commonly used deep learning methods for development of a short-term water demand forecast model for a real-world dataset. The algorithms studied in this work are (i) Multi-Layer Perceptron (MLP) (ii) Gated Recurrent Unit (GRU) (iii) Long Short-Term Memory (LSTM) (iv) Convolutional Neural Networks (CNN) and (v) the hybrid algorithm CNN-LSTM. Optimal supervised learning framework required for forecasting the one day ahead water demand for the study area is also identified. The dataset used in this study is from Hillsborough County, Florida, US. The water demand data was available for a duration of 10 months and the data frequency is about once per hour. These algorithms were evaluated based on the (1) Mean Absolute Percentage Error (MAPE) and (ii) Root Mean Squared Error (RMSE) values. Visual comparison of the predicted and true demand plots was also employed to check the prediction accuracy. It was observed that, the RMSE and MAPE values were minimal for the supervised learning framework that used the previous 24-hour data as input. Also, with respect to the forecast accuracy, CNN-LSTM performed better than the other methods for demand forecast, followed by MLP. MAPE values for the developed deep learning models ranged from 5% to 25%. The quantity, frequency and quality of data was also found to have substantial impact on the accuracy of the forecast models developed. In the CNN-LSTM based forecast model, the CNN component was found to effectively extract the inherent characteristics of historical water consumption data such as the trend and seasonality, while the LSTM part was able to reflect on the long-term historical process and future trend. Thus, its water demand prediction accuracy was improved compared to the other methods such as GRU, MLP, CNN and LSTM.</p>

scholarly journals Using Machine Learning Methods in the Financial Market for Technical Analysis Based on Hybrid Models

2020 ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hybrid Model ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Input Structure ◽  
Long Short Term Memory

In this study, it is presented a new hybrid model based on deep neural networks to predict the direction and magnitude of the Forex market movement in the short term. The overall model presented is based on the scalping strategy and is provided for high frequency transactions. The proposed hybrid model is based on a combination of three models based on deep neural networks. The first model is a deep neural network with a multi-input structure consisting of a combination of Long Short Term Memory layers. The second model is a deep neural network with a multi-input structure made of a combination of one-dimensional Convolutional Neural network layers. The third model has a simpler structure and is a multi-input model of the Multi-Layer Perceptron layers. The overall model was also a model based on the majority vote of three top models. This study showed that models based on Long Short-Term Memory layers provided better results than the other models and even hybrid models with more than 70% accurate.

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