Demand forecasting for fast-moving products in grocery retail

Demand forecasting is a critically important task in grocery retail. Accurate forecasts allow the retail companies to reduce their product spoilage, as well as maximize their profits. Fast-moving products, or products with a lot of sales and fast turnover, are particularly important to forecast accurately due to their high sales volumes. We investigate dynamic harmonic regression, Poisson GLM with elastic net, MLP and two-layer LSTM in fast-moving product demand forecasting against the naive seasonal forecasting baseline. We evaluate two modes of seasonality modelling in neural networks: Fourier series against seasonal decomposition. We specify the full procedure for comparing forecasting models in a collection of product-location sales time series, involving two-stage cross-validation, and careful hyperparameter selection. We use Halton sequences for neural network hyperparameter selection. We evaluate the model results in demand forecasting using hypothesis testing, bootstrapping, and rank comparison methods. The experimental results suggest that the dynamic harmonic regression produces superior results in comparison to Poisson GLM, MLP and two-layer LSTM models for demand forecasting in fast-moving products with long sales histories. We additionally show that deseasonalization results in better forecasts in comparison to Fourier seasonality modelling in neural networks

Approaching sales forecasting using recurrent neural networks and transformers

Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the precise execution of the corresponding downstream processes (inbound and outbound planning, inventory placement, network planning, etc). We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques and the Corporaci\'on Favorita data set, published as part of a Kaggle competition. Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort. Additionally, we describe a training trick for making the model more time independent and hence improving generalization over time. The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition.Comment: Accepted for publication in Expert Systems and Application

Використання темпоральних згорткових нейронних мереж в системах прогнозування продажів

Магістерська дисертація: 74 с., 22 табл., 14 рис. 22 джерел Об'єкт дослідження – дані продажів магазинів роздрібної торгівлі Предмет дослідження – системи прогнозування продажів з застосуванням нейронних мереж Мета роботи – дослідити точність згорткових мереж в системах прогнозування продажів. Метод дослідження – побудова прогнозних моделей. Актуальність роботи - створення програмного модуля, що дозволятиме робити прогнози продажів магазинів роздрібної торгівлі. Програмний продукт реалізовано за допомогою мови програумвання Python, на базі фреймворку Tensorflow. Обробка даних виконується за допомогою бібліотеки pandas Отримані результати – розроблено систему прогнозування продажів для магазинів роздрібної торгівлі та інтернет магазинів. Проведено порівняльний аналіз мереж LSTM та TCNTheme: “Using temporal convolutional neural networks for sales forecasting systems” Thesis:74 pages, 22 tables, 14 picture, 22 cited sources. Object of the study – retail and e-commerce sales data. Subject of research – sales forecasting systems. The purpose of the work is to develop a system for analysis and forecasting of e- commerce and retail sales using neural networks. The method of research – development of forecasting models. Actuality is to work is to create software module that will allow you to make sales forecasting for pricing decisions. Application is implemented using the Python programming language, based on the Tensorflow framework. Data preprocessing is executed using pandas library Obtained results - the information-analytical system for sales modeling and forecasting for e-commerce. LSTM and TCN architectures comparison analysis

A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks

Demand forecasting is becoming increasingly important as firms launch new products with short life cycles more frequently. This paper provides a framework based on state-of-the-art techniques that enables firms to use quantitative methods to forecast sales of newly launched, short-lived products that are similar to previous products when there is limited availability of historical sales data for the new product. In addition to exploiting historical data using time-series clustering, we perform data augmentation to generate sufficient sales data and consider two quantitative cluster assignment methods. We apply one traditional statistical (ARIMAX) and three machine learning methods based on deep neural networks (DNNs) – long short-term memory, gated recurrent units, and convolutional neural networks. Using two large data sets, we investigate the forecasting methods’ comparative performance and, for the larger data set, show that clustering generally results in substantially lower forecast errors. Our key empirical finding is that simple ARIMAX considerably outperforms the more advanced DNNs, with mean absolute errors up to 21%–24% lower. However, when adding Gaussian white noise in our robustness analysis, we find that ARIMAX's performance deteriorates dramatically, whereas the considered DNNs display robust performance. Our results provide insights for practitioners on when to use advanced deep learning methods and when to use traditional methods

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words

Forecasting Time Series Data Using Bayesian Regularization

Forecasting or predicting future events is important to take into account in order for an activity to proceed properly. Flights predict the weather forecast, the banking industry predicts the price of currency, the health world predicts the disease, the retail business predicts total sales. prediction or forecasting of events is calculated using past data, usually in the form of time series. Artificial neural networks are capable of forecasting time-series data. Forecasting results with artificial neural network is influenced from the network architecture model is determined, one of which determination of training function. This study uses the bayesian regularization training function to forecast time clock data with several layer count models and the number of neurons.The results obtained with the number of 3 layers and each neuron of 36, 12, 6 for the best process performance, and the number of neurons 24, 12, 6 for the shortest iteration process

Application of artificial neural networks in sales forecasting

The aim of the work presented in this paper is to forecast sales volumes as accurately as possible and as far into the future as possible. The choice of network topology was Silva's adaptive backpropagation algorithm and the network architectures were selected by genetic algorithms (GAs). The networks were trained to forecast from 1 month to 6 months in advance and the performance of the network was tested after training. The test results of artificial neural networks (ANNs) are compared with the time series smoothing methods of forecasting using several measures of accuracy. The outcome of the comparison proved that the ANNs generally perform better than the time series smoothing methods of forecasting. Further recommendations resulting from this paper are presentedpublished_or_final_versio

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors