The Impact of Bullwhip Effect on the Cash Flow in Two-Parallel Supply Chain Systems with the Competition Effect

While considering the competition effect and market share, this study discusses how the cash flow bullwhip effect (CFBE) is impacted in two-product and two-parallel supply chain systems by comparing the situation that it has one kind of product in two-level supply chain (SC). Specifically, the study aimed to examine two-product and two-parallel SC systems that include two suppliers and two retailers. Assuming that the demand function is a linear relationship of price self-sensitivity coefficient and price cross-sensitivity coefficient, which is an AR(1) process, two retailers share the demand. After that, the quantitative equation of the CFBE was deduced from two-product and two-parallel SC systems. Finally, we get the condition that the competition effect and the market share increase or decrease the CFBE, which was in contrast to the situation without the competition effect and the market share. The paper suggested that the manager can cooperate with their partner if two products are substitutable. On the other hand, the firm should improve the forecasting accuracy of the customer’s demand and improve the service quality so that it can increase the market share and reduce the CFBE in two-parallel SC systems.

Optimization of an unscented Kalman filter for an embedded platform

The unscented Kalman filter (UKF) is finding increased application in biological fields. While realizing a complex UKF system in a low-power embedded platform offers many potential benefits including wearability, it also poses significant design challenges. Here we present a method for optimizing a UKF system for realization in an embedded platform. The method seeks to minimize both computation time and error in UKF state reconstruction and forecasting. As a case study, we applied the method to a model for the rat sleep-wake regulatory system in which 432 variants of the UKF over six different variables are considered. The optimization method is divided into three stages that assess computation time, state forecast error, and state reconstruction error. We apply a cost function to variants that pass all three stages to identify a variant that computes 27 times faster than the reference variant and maintains required levels of state estimation and forecasting accuracy. We draw the following insights: 1) process noise provides leeway for simplifying the model and its integration in ways that speed computation time while maintaining state forecasting accuracy, 2) the assimilation of observed data during the UKF correction step provides leeway for simplifying the UKF structure in ways that speed computation time while maintaining state reconstruction accuracy, and 3) the optimization process can be accelerated by decoupling variables that directly impact the underlying model from variables that impact the UKF structure.

Detection of Road Traffic Anomalies Based on Computational Data Science

The development of 5G has enabled the autonomous vehicles (AVs) to have full control over all functions. The AV acts autonomously and collects travel data based on various smart devices and sensors, with the goal of enabling it to operate under its own power. However, the collected data is affected by several sources that degrade the forecasting accuracy. To manage large amounts of traffic data in different formats, a computational data science approach (CDS) is proposed. The computational data science scheme introduced to detect anomalies in traffic data that negatively affect traffic efficiency. The combination of data science and advanced artificial intelligence techniques, such as deep leaning provides higher degree of data anomalies detection which leads to reduce traffic congestion and vehicular queuing. The main contribution of the CDS approach is summarized in detection of the factors that caused data anomalies early to avoid long- term traffic congestions. Moreover, CDS indicated a promoting results in various road traffic scenarios.

An approach for forecasting the Onion Price volatility in Indian Wholesale Markets using hybrid GARCH-LSTM Deep learning models

The fluctuations in the Onion prices have led to political and economic ramifications in countries such as India. In this study, we intend to estimate and then forecast the price volatility of Onion sales prices in major Indian wholesale markets. Initially, we take daily price data from major vegetable wholesale markets across India and simulate them to compute corresponding daily conditional volatilities using the traditional GARCH method. We then forecast the volatilities for the upcoming 10,15 and 21 days using the same traditional GARCH method and compare its forecasting accuracy with recent AI-led models. According to our comparisons, the deep learning-based LSTM model with various configurations provides superior results when compared to other traditional models with the highest accuracy in more than 70% of the cases. We expect that the given study could help the policymakers in managing sufficient buffer stock levels and the food supply chain stakeholders in hedging against the overall market risks due to the fluctuations in prices.

The role of transition regime models for corn prices forecasting

Given the relevance of corn for food and fuel industries, analysts and scholars are constantly comparing the forecasting accuracy of econometric models. These exercises test not only for the use of new approaches and methods, but also for the addition of fundamental variables linked to the corn market. This paper compares the accuracy of different usual models in financial macro-econometric literature for the period between 1995 and 2017. The main contribution lies in the use of transition regime models, which accommodate structural breaks and perform better for corn price forecasting. The results point out that the best models as those which consider not only the corn market structure, or macroeconomic and financial fundamentals, but also the non-linear trend and transition regimes, such as threshold autoregressive models.

Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network

This study examines the performance of seasonal autoregressive integrated moving average (SARIMA), multilayer perceptron neural networks (MLPNN), and hybrid SARIMA-MLPNN model(s) in modelling and forecasting inflation rate using the monthly consumer price index (CPI) data from 2010 to 2019 obtained from the South African Reserve Bank (SARB). The forecast errors in inflation rate forecasting are analyzed and compared. The study employed root mean squared error (RMSE) and mean absolute error (MAE) as performance measures. The results indicate that significant improvements in forecasting accuracy are obtained with the hybrid model (SARIMA-MLPNN) compared to the SARIMA and MLPNN. The MLPNN model outperformed the SARIMA model. However, the hybrid SARIMA-MLPNN model outperformed both the SARIMA and MLPNN in terms of forecasting accuracy/accuracy performance.

A big data analysis of Twitter data during premier league matches: do tweets contain information valuable for in-play forecasting of goals in football?

Data-related analysis in football increasingly benefits from Big Data approaches and machine learning methods. One relevant application of data analysis in football is forecasting, which relies on understanding and accurately modelling the process of a match. The present paper tackles two neglected facets of forecasting in football: Forecasts on the total number of goals and in-play forecasting (forecasts based on within-match information). Sentiment analysis techniques were used to extract the information reflected in almost two million tweets from more than 400 Premier League matches. By means of wordclouds and timely analysis of several tweet-based features, the Twitter communication over the full course of matches and shortly before and after goals was visualized and systematically analysed. Moreover, several forecasting models including a random forest model have been used to obtain in-play forecasts. Results suggest that in-play forecasting of goals is highly challenging, and in-play information does not improve forecasting accuracy. An additional analysis of goals from more than 30,000 matches from the main European football leagues supports the notion that the predictive value of in-play information is highly limited compared to pre-game information. This is a relevant result for coaches, match analysts and broadcasters who should not overestimate the value of in-play information. The present study also sheds light on how the perception and behaviour of Twitter users change over the course of a football match. A main result is that the sentiment of Twitter users decreases when the match progresses, which might be caused by an unjustified high expectation of football fans before the match.

Horizontal Grid Spacing Comparison Among Random Forest Algorithms to Nowcast Cloud-to-Ground Lightning Occurrence

This paper discusses the use of Random Forest (RF), a popular Machine Learning (ML) algorithm, to perform spatially explicit nowcasting of cloud-to-ground lightning occurrence. An application to the Italian territory and the surrounding seas is presented. Specifically, 1-hour ahead lightning occurrences over the months of August, September and October from 2017 to 2019 have been modelled using a dataset including geo-environmental features. Results obtained with three different spatial resolutions have been compared, for nowcasting both positive and negative strokes. The features’ importance resulting from the best RF models showed how datadriven models are able to identify the relationships between meteorological variables, in agreement with previous physically based knowledge of the phenomenon. The encouraging results obtained in terms of forecasting accuracy support the idea to use ML-based algorithms in early warning procedures for disaster risk management.

Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach

Existing research has shown that combination can effectively improve tourism forecasting accuracy compared with single model. However, the model uncertainty and structural instability in combination for out-of-sample tourism forecasting may influence the forecasting performance. This paper proposes a novel forecast combination approach based on time-varying jackknife model averaging (TVJMA), which can more efficiently handle structural changes and nonstationary trends in tourism data. Using Hong Kong tourism demand from five major tourism source regions as an empirical study, we investigate whether our proposed nonparametric TVJMA-based approach can improve tourism forecasting accuracy further. Empirical results show that the proposed TVJMA-based approach outperforms other competitors including single model and three combination methods in most cases. Findings indicate the outstanding performance of our method is robust to various forecasting horizons and different estimation periods.

An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning

The mechanisms of data analytics and machine learning can allow for a profound conceptualization of viruses (such as pathogen transmission rate and behavior). Consequently, such models have been widely employed to provide rapid and accurate viral spread forecasts to public health officials. Nevertheless, the capability of these algorithms to predict outbreaks is not capable of long-term predictions. Thus, the development of superior models is crucial to strengthen disease prevention strategies and long-term COVID-19 forecasting accuracy. This paper provides a comparative analysis of COVID-19 forecasting models, including the Deep Learning (DL) approach and its examination of the circulation and transmission of COVID-19 in the Kingdom of Saudi Arabia (KSA), Kuwait, Bahrain, and the UAE.