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How To: Machine Learning-Driven Demand Forecasting

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Usual statistical models apply a set of known relationships to a dataset. For example, exponential smoothing will have its way of estimating the underlying demand level and trend. On the other hand, machine learning is about letting an algorithm understand a dataset and its underlying relationships on its own. A Machine Learning algorithm will run through a dataset, look at data features, and (try to) pick up any underlying relationship. Choosing the correct data to feed to your model is tremendously important.


Demand Forecasting for the Modern Supply Chain

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Demand forecasting refers to the process of planning and predicting goods and materials demand to help businesses stay as profitable as possible. Without strong demand forecasting, companies risk carrying wasteful and costly surplus – or losing opportunities because they have failed to anticipate customer needs, preferences, and purchasing intent. Demand forecasting professionals have specialized skills and experience. When those skills are augmented with modern supply chain technologies and predictive analytics, supply chains can become more competitive and streamlined than ever. In the wake of the pandemic, companies are in an exceptionally fast-moving business climate.


Demand forecasting: Using machine learning to predict retail sales

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Too many items and too few items are both scenarios that are bad for business. Massive incremental profit can be unlocked by retailers managing orders and inventory effectively. But as this requires the processing of data for a huge number of stock keeping units (SKUs), which often include perishable goods and items that are ordered daily, it is also a significant challenge. Retailers used to rely solely on the data from previous years to predict future sales (and therefore manage their inventory), but this method is only useful up to a point. However, machine learning has now evolved to the stage that it can provide accurate predictive models using different signals based on how they influence purchases.


Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting

arXiv.org Machine Learning

We present a winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm. The day-ahead load forecasting approach is based on online forecast combination of multiple point prediction models. It contains four steps: i) data cleaning and preprocessing, ii) a holiday adjustment procedure, iii) training of individual forecasting models, iv) forecast combination by smoothed Bernstein Online Aggregation (BOA). The approach is flexible and can quickly adopt to new energy system situations as they occurred during and after COVID-19 shutdowns. The pool of individual prediction models ranges from rather simple time series models to sophisticated models like generalized additive models (GAMs) and high-dimensional linear models estimated by lasso. They incorporate autoregressive, calendar and weather effects efficiently. All steps contain novel concepts that contribute to the excellent forecasting performance of the proposed method. This holds particularly for the holiday adjustment procedure and the fully adaptive smoothed BOA approach.


Probabilistic water demand forecasting using quantile regression algorithms

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Machine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression algorithms are statistical and machine learning algorithms that can provide probabilistic forecasts in a straightforward way, and have not been applied so far for urban water demand forecasting. In this work, we aim to fill this gap by automating and extensively comparing several quantile-regression-based practical systems for probabilistic one-day ahead urban water demand forecasting. For designing the practical systems, we use five individual algorithms (i.e., the quantile regression, linear boosting, generalized random forest, gradient boosting machine and quantile regression neural network algorithms), their mean combiner and their median combiner.


Semantic XAI for contextualized demand forecasting explanations

arXiv.org Artificial Intelligence

The paper proposes a novel architecture for explainable AI based on semantic technologies and AI. We tailor the architecture for the domain of demand forecasting and validate it on a real-world case study. The provided explanations combine concepts describing features relevant to a particular forecast, related media events, and metadata regarding external datasets of interest. The knowledge graph provides concepts that convey feature information at a higher abstraction level. By using them, explanations do not expose sensitive details regarding the demand forecasting models. The explanations also emphasize actionable dimensions where suitable. We link domain knowledge, forecasted values, and forecast explanations in a Knowledge Graph. The ontology and dataset we developed for this use case are publicly available for further research.


Time Series Demand Forecasting

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Register for our blog to get new articles as we release them. Demand Forecasting is a technique for estimation of probable demand for a product or services. It is based on the analysis of past demand for that product or service in the present market condition. Demand forecasting should be done on a scientific basis and facts and events related to forecasting should be considered. After gathering information about various aspects of the market and demand based on the past, is possible to estimate future demand.


Demand Forecasting For Retail: A Deep Dive

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I know for sure that human behavior could be predicted with data science and machine learning. Taking a look at human behavior from a sales data analysis perspective, we can get more valuable insights than from social surveys. In this article, I want to show how machine learning approaches can help with customer demand forecasting. Since I have experience in building forecasting models for retail field products, I'll use a retail business as an example. Moreover, considering uncertainties related to the COVID-19 pandemic, I'll also describe how to enhance forecasting accuracy.


4 ways Artificial Intelligence is reshaping demand forecasting in retail - Indiaretailing.com

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Artificial intelligence (AI) is the technology of today, the story of 2010 and the excitement of tomorrow. The past decade will be reminisced as an era where machines began their journey on the path of intelligence – proficient in learning, executing, and'thinking' like humans do. The digitalization of the Retail Industry has been changing in recent years with augmented efficiency, rapidity and accuracy across every branch of business domain. Through prognostic analytics and innovative data exploration, we are now able to make all data-focused business resolutions. AI in the domain of retail has enabled industries to access high levels of data information which has improved retail operations and given business better opportunities.


Demand Forecasting of individual Probability Density Functions with Machine Learning

arXiv.org Machine Learning

Demand forecasting is a central component for many aspects of supply chain operations, as it provides crucial input for subsequent decision making like ordering processes. While machine learning methods can significantly improve prediction accuracy over traditional time series forecasting, the calculated predictions are often just point estimations for the conditional mean of the underlying probability distribution, and the most powerful approaches, like deep learning, are usually opaque in terms of how its individual predictions can be interpreted. Using the novel supervised machine learning method "Cyclic Boosting", complete individual probability density functions can be predicted instead of single numbers. While metrics evaluating point estimates are widely used, methods for assessing the accuracy of predicted distributions are rare and this work proposes new techniques for both qualitative and quantitative evaluation methods. Additionally, each single prediction obtained with this framework is explainable. This is a major benefit in particular for practitioners, as this allows them to avoid "black-box" models and understand the contributing factors for each individual prediction.