Implementation of the Support Vector Regression Algorithm And Particle Swarm Optimization In Sales Forecasting

Abstract: Sales forecasting is an important element in planning business strategies that have the possibility of happening in the future. But how to improve the accuracy of the current forecasting process is still a big question mark for companies. This study uses the Support Vector Regression algorithm because it is one of the forecasting techniques that is categorized as good compared to other techniques. The Particle Swarm Optimization algorithm is integrated for attribute optimization so that forecasting accuracy can be better. Based on the test results, it was found that SVR-PSO produced an RMSE value of 9.40.Keywords: sales forecasting; support vector regression; particle swarm optimization.Abstrak: Peramalan penjualan merupakan elemen penting dalam perencanaan strategi bisnis yang memiliki kemungkinan terjadi di masa depan. Tetapi bagaimana meningkatkan ketepatan proses peramalan saat ini masih menjadi tanda tanya besar bagi perusahaan. Penelitian ini menggunakan algoritme Support Vector Regression karena merupakan salah satu teknik peramalan yang dikategorikan baik dibandingkan dengan teknik yang lainnya. Algoritme Particle Swarm Optimization diintegrasikan untuk optimasi atribut agar akurasi peramalan dapat lebih baik. Berdasarkan hasil pengujian, didapatkan SVR-PSO menghasilkan nilai RMSE 9.40.Kata kunci : peramalan penjualan; support vector regression; particle swarm optimization

Distributional regression for demand forecasting in e-grocery

Ulrich M, Jahnke H, Langrock R, Pesch R, Senge R. Distributional regression for demand forecasting in e-grocery. Universität Bielefeld Working Papers in Economics and Management. Vol 09-2018. Bielefeld: Bielefeld University, Department of Business Administration and Economics; 2019.E-grocery offers customers an alternative to traditional brick-and-mortar grocery retailing. Customers select e-grocery for convenience, making use of the home delivery at a selected time slot. In contrast to brick-and-mortar retailing, in e-grocery on-stock information for stock keeping units (SKUs) becomes transparent to the customer before substantial shopping effort has been invested, thus reducing the personal cost of switching to another supplier. As a consequence, compared to brick-and-mortar retailing, on-stock availability of SKUs has a strong impact on the customer’s order decision, resulting in higher strategic service level targets for the e-grocery retailer. To account for these high service level targets, we propose a suitable model for accurately predicting the extreme right tail of the demand distribution, rather than providing point forecasts of its mean. Specifically, we propose the application of distributional regression methods— so-called Generalised Additive Models for Location, Scale and Shape (GAMLSS)—to arrive at the cost-minimising solution according to the newsvendor model. As benchmark models we consider linear regression, quantile regression, and some popular methods from machine learning. The models are evaluated in a case study, where we compare their out-of-sample predictive performance with regard to the service level selected by the e-grocery retailer considered

Deep learning based customer preferences analysis in industry 4.0 environment

Customer preferences analysis and modelling using deep learning in edge computing environment are critical to enhance customer relationship management that focus on a dynamically changing market place. Existing forecasting methods work well with often seen and linear demand patterns but become less accurate with intermittent demands in the catering industry. In this paper, we introduce a throughput deep learning model for both short-term and long-term demands forecasting aimed at allowing catering businesses to be highly efficient and avoid wastage. Moreover, detailed data collected from a business online booking system in the past three years have been used to train and verify the proposed model. Meanwhile, we carefully analyzed the seasonal conditions as well as past local or national events (event analysis) that could have had critical impact on the sales. The results are compared with the best performing forecast methods Xgboost and autoregressive moving average model (ARMA), and they suggest that the proposed method significantly improves demand forecasting accuracy (up to 80%) for dishes demand along with reduction in associated costs and labor allocation

A 30-Year Agroclimatic Analysis of the Snake River Valley American Viticultural Area - Descriptive and Predictive Methods

Climate change poses serious threats to global agriculture, however some localities and crops may benefit from increasing temperatures. Grape production in southern Idaho may be a beneficial example as vineyard acreage has increased over 300% since the designation of the Snake River American Viticultural Area (SRVAVA) in 2007. We perform a statistical characterization of agroclimate within the SRVAVA that centers around four primary objectives: utilization of a novel, 30-year high resolution climate dataset to provide insight and agrometrics unavailable at coarser resolutions, climatic implications of the unique topography within the SRVAVA, identification of statistical trends, and correlation of SRVAVA climate to large-scale climate indicators such as the El Nino Southern Oscillation (ENSO). In Chapter 3 we build on the identified correlations to large scale climate and utilize a long short-term memory (LSTM) model in conjunction with empirical mode decomposition (EMD) to create a novel, data driven method to forecast regional temperature trends with lead times up to one year. Favorable results for local viticulture include an increase in growing degree days and season length, as well as reduced frequency of freezing events. Possible disadvantages include increased risk to shoulder season freezing events with warmer winters, increased magnitude of strong freezing events, mid-season heat stress, and higher susceptibility to powdery mildew outbreaks. Additionally, with strong correlations identified with large-scale climate indicators, we find EMD an effective method to increase modeling power by using multiple frequencies of the signals as input into a LSTM machine learning algorithm that can accurately predict temperature trends up to one year in advance. This climatic characterization and modeling framework could potentially inform many agricultural management decisions such as cultivar choice, vineyard site selection, fungicide spray timing, irrigation strategy, and canopy management

Grapevine yield prediction using image analysis - improving the estimation of non-visible bunches

Yield forecast is an issue of utmost importance for the entire grape and wine sectors. There are several methods for vineyard yield estimation. The ones based on estimating yield components are the most commonly used in commercial vineyards. Those methods are generally destructive and very labor intensive and can provide inaccurate results as they are based on the assessment of a small sample of bunches. Recently, several attempts have been made to apply image analysis technologies for bunch and/or berries recognition in digital images. Nonetheless, the effectiveness of image analysis in predicting yield is strongly dependent of grape bunch visibility, which is dependent on canopy density at fruiting zone and on bunch number, density and dimensions. In this work data on bunch occlusion obtained in a field experiment is presented. This work is set-up in the frame of a research project aimed at the development of an unmanned ground vehicle to scout vineyards for non-intrusive estimation of canopy features and grape yield. The objective is to evaluate the use of explanatory variables to estimate the fraction of non-visible bunches (bunches occluded by leaves). In the future, this estimation can potentially improve the accuracy of a computer vision algorithm used by the robot to estimate total yield. In two vineyard plots with Encruzado (white) and Syrah (red) varieties, several canopy segments of 1 meter length were photographed with a RGB camera and a blue background, close to harvest date. Out of these images, canopy gaps (porosity) and bunches’ region of interest (ROI) files were computed in order to estimate the corresponding projected area. Vines were then defoliated at fruiting zone, in two steps and new images were obtained before each step. Overall the area of bunches occluded by leaves achieved mean values between 67% and 73%, with Syrah presenting the larger variation. A polynomial regression was fitted between canopy porosity (independent variable) and percentage of bunches not occluded by leaves which showed significant R2 values of 0.83 and 0.82 for the Encruzado and Syrah varieties, respectively. Our results show that the fraction of non-visible bunches can be estimated indirectly using canopy porosity as explanatory variable, a trait that can be automatically obtained in the future using a laser range finder deployed on the mobile platforminfo:eu-repo/semantics/publishedVersio

Tracking and modelling prices using web-scraped price microdata : towards automated daily consumer price index forecasting

With the increasing relevance and availability of on-line prices that we see today, it is natural to ask whether the prediction of the consumer price index (CPI), or related statistics, may usefully be computed more frequently than existing monthly schedules allow for. The simple answer is ‘yes’, but there are challenges to be overcome first. A key challenge, addressed by our work, is that web-scraped price data are extremely messy and it is not obvious, a priori, how to reconcile them with standard CPI statistics. Our research focuses on average prices and disaggregated CPI at the level of product categories (lager, potatoes, etc.) and develops a new model that describes the joint time evolution of latent daily log-inflation rates driving prices seen on the Internet and prices recorded in official surveys, with the model adapting to various product categories. Our model reveals the differing levels of dynamic behaviour across product category and, correspondingly, differing levels of predictability. Our methodology enables good prediction of product-category-specific CPI immediately before their release. In due course, with increasingly complete web-scraped data, combined with the best survey data, the prospect of more frequent intermonth aggregated CPI prediction is an achievable goal

Big Data in Bioeconomy

This edited open access book presents the comprehensive outcome of The European DataBio Project, which examined new data-driven methods to shape a bioeconomy. These methods are used to develop new and sustainable ways to use forest, farm and fishery resources. As a European initiative, the goal is to use these new findings to support decision-makers and producers – meaning farmers, land and forest owners and fishermen. With their 27 pilot projects from 17 countries, the authors examine important sectors and highlight examples where modern data-driven methods were used to increase sustainability. How can farmers, foresters or fishermen use these insights in their daily lives? The authors answer this and other questions for our readers. The first four parts of this book give an overview of the big data technologies relevant for optimal raw material gathering. The next three parts put these technologies into perspective, by showing useable applications from farming, forestry and fishery. The final part of this book gives a summary and a view on the future. With its broad outlook and variety of topics, this book is an enrichment for students and scientists in bioeconomy, biodiversity and renewable resources

Sistem Pendukung Keputusan Penentuan Kombinasi Paket Produk Pertanian Menggunakan Algoritma Apriori

Sektor pertanian di Indonesia yang memiliki potensi hasil tani tinggi ternyata memiliki kendala yaitu kurangnya pemanfaatan teknologi informasi. Data transaksi yang mengendap tidak dimanfaatkan,  jika data ini diolah maka akan memberikan manfaat khususnya dalam memaksimalkan keuntungan penjualan produk pertanian. Penelitian ini bertujuan untuk merancang sebuah Sistem Pendukung Keputusan (SPK) kombinasi paket produk pertanian dengan memanfaatkan data transaksi yang sudah ada menggunakan algoritma apriori. Penelitian ini mengembangkan beberapa fitur inovasi agar pembangkitan item set lebih cepat dan menghasilkan aturan asosiasi yang mudah dipahami oleh pengguna. Hasil dari penelitian ini didapatkan rekomendasi paket produk pertanian dengan jumlah kemunculan transaksi 3, nilai support 25 dan nilai confidence 100, dan uji lift ratio 2,40. Jumlah data set dari fitur inovasi periode tanggal transaksi di mana semakin banyak data transaksi yang ada dan parameternya semakin tinggi maka akan semakin baik hasilnya. Presentasi dari penerimaan pengguna sebesar 73% artinya SPK ini diterima dan berjalan dengan baik

The Nexus Between Security Sector Governance/Reform and Sustainable Development Goal-16

This Security Sector Reform (SSR) Paper offers a universal and analytical perspective on the linkages between Security Sector Governance (SSG)/SSR (SSG/R) and Sustainable Development Goal-16 (SDG-16), focusing on conflict and post-conflict settings as well as transitional and consolidated democracies. Against the background of development and security literatures traditionally maintaining separate and compartmentalized presence in both academic and policymaking circles, it maintains that the contemporary security- and development-related challenges are inextricably linked, requiring effective measures with an accurate understanding of the nature of these challenges. In that sense, SDG-16 is surely a good step in the right direction. After comparing and contrasting SSG/R and SDG-16, this SSR Paper argues that human security lies at the heart of the nexus between the 2030 Agenda of the United Nations (UN) and SSG/R. To do so, it first provides a brief overview of the scholarly and policymaking literature on the development-security nexus to set the background for the adoption of The Agenda 2030. Next, it reviews the literature on SSG/R and SDGs, and how each concept evolved over time. It then identifies the puzzle this study seeks to address by comparing and contrasting SSG/R with SDG-16. After making a case that human security lies at the heart of the nexus between the UN’s 2030 Agenda and SSG/R, this book analyses the strengths and weaknesses of human security as a bridge between SSG/R and SDG-16 and makes policy recommendations on how SSG/R, bolstered by human security, may help achieve better results on the SDG-16 targets. It specifically emphasizes the importance of transparency, oversight, and accountability on the one hand, and participative approach and local ownership on the other. It concludes by arguing that a simultaneous emphasis on security and development is sorely needed for addressing the issues under the purview of SDG-16