Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. (Wikipedia)
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.
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.
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.
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.
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 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.
LLamasoft published the results of a global retail supply chain study, which revealed that 73% of retailers believe artificial intelligence (AI) and machine learning can add significant value to their demand forecasting processes. Meanwhile, over half say it will improve 8 other critical supply chain capabilities. The research also found that while 56% of overperforming retailers, also known as'retail winners', use technology to model contingency plans for severe supply chain interruptions, a mere 31% of retailers who are not overperforming do the same. Overall, 56% of retailers surveyed are struggling with the ability to respond to rapid shifts, and the lack of flexibility has cost them during the disruptions such as COVID-19, with many seeing a huge drop in revenue as a result. In addition, 73% of'retail winners' have the foresight and ability to monitor capacity, which allows them to prepare for sudden shifts in demand and supply, compared to 35% of'other' or'under-performing' retailers.
Hierarchical forecasting methods have been widely used to support aligned decision-making by providing coherent forecasts at different aggregation levels. Traditional hierarchical forecasting approaches, such as the bottom-up and top-down methods, focus on a particular aggregation level to anchor the forecasts. During the past decades, these have been replaced by a variety of linear combination approaches that exploit information from the complete hierarchy to produce more accurate forecasts. However, the performance of these combination methods depends on the particularities of the examined series and their relationships. This paper proposes a novel hierarchical forecasting approach based on machine learning that deals with these limitations in three important ways. First, the proposed method allows for a non-linear combination of the base forecasts, thus being more general than the linear approaches. Second, it structurally combines the objectives of improved post-sample empirical forecasting accuracy and coherence. Finally, due to its non-linear nature, our approach selectively combines the base forecasts in a direct and automated way without requiring that the complete information must be used for producing reconciled forecasts for each series and level. The proposed method is evaluated both in terms of accuracy and bias using two different data sets coming from the tourism and retail industries. Our results suggest that the proposed method gives superior point forecasts than existing approaches, especially when the series comprising the hierarchy are not characterized by the same patterns.
Verdis is a supply chain data analytics software that uses Opportunity Intelligence, a proprietary technology model, that identifies opportunities for maximizing performance in the supply chain. Opportunity intelligence utilizes Artificial Intelligence and Machine Learning to analyze data, identify patterns, build causal relationships and then using augmented analytics to push insights to the decision makers. These insights are powerful action drivers that help the SCM function to optimize the performance between the responsiveness and efficiency of the operations at all levels and across different stages. Therefore whether it is identifying the areas for optimizing inventory levels or SCM costs, Verdis is able to provide predictions and give recommendations that help to elevate the performance delivery of the function. As it is driven by powerful learning algorithms, Verdis is constantly learning the individual business context of your organization which enables it to deliver your context-specific insights.
We present a scalable and robust Bayesian method for demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson algorithm, reduced to linear-time Kalman smoothing, which allows us to operate on several orders of magnitude larger problems than previous related work. In a study on large real-world sales datasets, our method outperforms competing approaches on fast and medium moving items. Papers published at the Neural Information Processing Systems Conference.