Imputing unknown competitor marketing activity with a Hidden Markov Chain

We demonstrate on a case study with two competing products at a bank how one can use a Hidden Markov Chain (HMC) to estimate missing information on a competitor's marketing activity. The idea is that given time series with sales volumes for products A and B and marketing expenditures for product A, as well as suitable predictors of sales for products A and B, we can infer at each point in time whether it is likely or not that marketing activities took place for product B. The method is successful in identifying the presence or absence of marketing activity for product B about 84% of the time. We allude to the issue of whether, if one can infer marketing activity about product B from knowledge of marketing activity for product A and of sales volumes of both products, the reverse might be possible and one might be able to impute marketing activity for product A from knowledge of that of product B. This leads to a concept of symmetric imputation of competing marketing activity. The exposition in this paper aims to be accessible and relevant to practitioners

Finding Influencers in Complex Networks: An Effective Deep Reinforcement Learning Approach

Maximizing influences in complex networks is a practically important but computationally challenging task for social network analysis, due to its NP- hard nature. Most current approximation or heuristic methods either require tremendous human design efforts or achieve unsatisfying balances between effectiveness and efficiency. Recent machine learning attempts only focus on speed but lack performance enhancement. In this paper, different from previous attempts, we propose an effective deep reinforcement learning model that achieves superior performances over traditional best influence maximization algorithms. Specifically, we design an end-to-end learning framework that combines graph neural network as the encoder and reinforcement learning as the decoder, named DREIM. Trough extensive training on small synthetic graphs, DREIM outperforms the state-of-the-art baseline methods on very large synthetic and real-world networks on solution quality, and we also empirically show its linear scalability with regard to the network size, which demonstrates its superiority in solving this problem

Intelligent Scheduling Method for Bulk Cargo Terminal Loading Process Based on Deep Reinforcement Learning

Funding Information: Funding: This research was funded by the National Natural Science Foundation of China under Grant U1964201 and Grant U21B6001, the Major Scientific and Technological Special Project of Hei-longjiang Province under Grant 2021ZX05A01, the Heilongjiang Natural Science Foundation under Grant LH2019F020, and the Major Scientific and Technological Research Project of Ningbo under Grant 2021Z040. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Sea freight is one of the most important ways for the transportation and distribution of coal and other bulk cargo. This paper proposes a method for optimizing the scheduling efficiency of the bulk cargo loading process based on deep reinforcement learning. The process includes a large number of states and possible choices that need to be taken into account, which are currently performed by skillful scheduling engineers on site. In terms of modeling, we extracted important information based on actual working data of the terminal to form the state space of the model. The yard information and the demand information of the ship are also considered. The scheduling output of each convey path from the yard to the cabin is the action of the agent. To avoid conflicts of occupying one machine at same time, certain restrictions are placed on whether the action can be executed. Based on Double DQN, an improved deep reinforcement learning method is proposed with a fully connected network structure and selected action sets according to the value of the network and the occupancy status of environment. To make the network converge more quickly, an improved new epsilon-greedy exploration strategy is also proposed, which uses different exploration rates for completely random selection and feasible random selection of actions. After training, an improved scheduling result is obtained when the tasks arrive randomly and the yard state is random. An important contribution of this paper is to integrate the useful features of the working time of the bulk cargo terminal into a state set, divide the scheduling process into discrete actions, and then reduce the scheduling problem into simple inputs and outputs. Another major contribution of this article is the design of a reinforcement learning algorithm for the bulk cargo terminal scheduling problem, and the training efficiency of the proposed algorithm is improved, which provides a practical example for solving bulk cargo terminal scheduling problems using reinforcement learning.publishersversionpublishe

Optimization of the marketing mix in the health care industry

This paper proposes data mining techniques to model the return on investment from various types of promotional spending to market a drug and then uses the model to draw conclusions on how the pharmaceutical industry might go about allocating marketing expenditures in a more efficient manner, potentially reducing costs to the consume

Movie Industry Economics: How Data Analytics Can Help Predict Movies’ Financial Success

Purpose: Data analytics techniques can help to predict movie success, as measured by box office sales or Oscar awards. Revenue prediction of a movie before its theatrical release is also an important indicator for attracting investors. While measures for predicting the success of a movie in box office sales and awards are widely missing, this study uses data analytics techniques to present a new measure for prediction of movies’ financial success.Methodology: Data were collected by web-scraping and text mining. Classification and Regression Tree (CART), Random Forests, Conditional Forests, and Gradient Boosting were used and a model for prediction of movies' financial success proposed. Content strategy and generating high profile reviews with complex themes can add to controversy and increase the chance of nomination for major movie awards, including Oscars.Findings/Contribution: Findings show that data analytics is key to predicting the success of movies. Although predicting sales based on data available before the release remains a difficult endeavor, even with state-of-the-art analytics technologies, it potentially reduces the risk of investors, studios and other stakeholders to select successful film candidates and have them chosen before the production process starts. The contribution of this study is to develop a model for predicting box office sales and the chance of nomination for winning Oscars. Practical Implications: Cinema managers and investors can use the proposed model as a guide for predicting movies’ financial success

Reciprocity in social networks - A case study in Tamil Nadu, India

International audienceThis case study takes us to Tamil Nadu (India) and discusses a Social Network Analysis (SNA) of a community of weavers in the village of Sankarapandiapuram. Subgroups and influential members are identified, and the analysis is placed in the context of the theory of social capital in economics. The presentation is self-contained and is accessible to readers with an introductory level of statistics

A Precise Stabilization Method for Linear Stochastic Time-Delay Systems

Based on ensuring the steady-state performance of the system, some dynamic performance indicators that have not yet been realized in linear stochastic systems with time-delay are discussed in this paper. First, in view of the relationship between system eigenvalues and system performances, the region stability is provided, which can reflect the dynamic performance of the systems. Second, the design scheme of the region stabilization controller is given based on the region stability, so that the closed-loop system has the corresponding dynamic performance. Third, this paper also designs an algorithm to deal with the situation in which the eigenvalues are located in the non-connected region in order to obtain more accurate control system dynamic performance. Finally, an example shows how the precise control method dominates the dynamic performance of the system

A High-Speed Vision-Based Sensor for Dynamic Vibration Analysis Using Fast Motion Extraction Algorithms

The development of image sensor and optics enables the application of vision-based techniques to the non-contact dynamic vibration analysis of large-scale structures. As an emerging technology, a vision-based approach allows for remote measuring and does not bring any additional mass to the measuring object compared with traditional contact measurements. In this study, a high-speed vision-based sensor system is developed to extract structure vibration signals in real time. A fast motion extraction algorithm is required for this system because the maximum sampling frequency of the charge-coupled device (CCD) sensor can reach up to 1000 Hz. Two efficient subpixel level motion extraction algorithms, namely the modified Taylor approximation refinement algorithm and the localization refinement algorithm, are integrated into the proposed vision sensor. Quantitative analysis shows that both of the two modified algorithms are at least five times faster than conventional upsampled cross-correlation approaches and achieve satisfactory error performance. The practicability of the developed sensor is evaluated by an experiment in a laboratory environment and a field test. Experimental results indicate that the developed high-speed vision-based sensor system can extract accurate dynamic structure vibration signals by tracking either artificial targets or natural features