Multistep ahead time series prediction

Time series analysis has been the subject of extensive interest in many fields ofstudy ranging from weather forecasting to economic predictions, over the past twocenturies. It has been fundamental to our understanding of previous patterns withindata and has also been used to make predictions in both the short and long termhorizons. When approaching such problems researchers would typically analyzethe given series for a number of distinct characteristics and select the most ap-propriate technique. However, the complexity of aligning a set of characteristicswith a method has increased in complexity with the advent of Machine Learningand the introduction of Multi-Step Ahead Prediction (MSAP). We examine themodel/strategy approaches which are currently applied to conduct multi-step aheadprediction in time series data and propose an alternative MSAP strategy known asMulti-Resolution Forecast Aggregation.Typically, when researchers propose an alternative strategy or method, they demon-strate it on a relatively small set of time series, thus the general breath of use isunknown. We propose a process that generates a diverse set of synthetic time se-ries, that will enable a robust examination of MRFA and other methods/strategies.This dataset in conjunction with a range of popular prediction methods and MSAPstrategies is then used to develop a meta learner that estimates the normalized meansquare error of the prediction approach for the given time serie

Multi-resolution forecast aggregation for time series in agri datasets

A wide variety of phenomena are characterized by time dependent dynamics that can be analyzed using time series methods. Various time series analysis techniques have been presented, each addressing certain aspects of the data. In time series analysis, forecasting is a challenging problem when attempting to estimate extended time horizons which effectively encapsulate multi-step-ahead (MSA) predictions. Two original solutions to MSA are the direct and the recursive approaches. Recent studies have mainly focused on combining previous methods as an attempt to overcome the problem of discarding sequential correlation in the direct strategy or accumulation of error in the recursive strategy. This paper introduces a technique known as Multi-Resolution Forecast Aggregation (MRFA) which incorporates an additional concept known as Resolutions of Impact. MRFA is shown to have favourable prediction capabilities in comparison to a number of state of the art methods

A methodology for validating diversity in synthetic time series generation

In order for researchers to deliver robust evaluations of time series models, it often requires high volumes of data to ensure the appropriate level of rigor in testing. However, for many researchers, the lack of time series presents a barrier to a deeper evaluation. While researchers have developed and used synthetic datasets, the development of this data requires a methodological approach to testing the entire dataset against a set of metrics which capture the diversity of the dataset. Unless researchers are confident that their test datasets display a broad set of time series characteristics, it may favor one type of predictive model over another. This can have the effect of undermining the evaluation of new predictive methods. In this paper, we present a new approach to generating and evaluating a high number of time series data. The construction algorithm and validation framework are described in detail, together with an analysis of the level of diversity present in the synthetic dataset

A review of the applications of multi-agent reinforcement learning in smart factories

The smart factory is at the heart of Industry 4.0 and is the new paradigm for establishing advanced manufacturing systems and realizing modern manufacturing objectives such as mass customization, automation, efficiency, and self-organization all at once. Such manufacturing systems, however, are characterized by dynamic and complex environments where a large number of decisions should be made for smart components such as production machines and the material handling system in a real-time and optimal manner. AI offers key intelligent control approaches in order to realize efficiency, agility, and automation all at once. One of the most challenging problems faced in this regard is uncertainty, meaning that due to the dynamic nature of the smart manufacturing environments, sudden seen or unseen events occur that should be handled in real-time. Due to the complexity and high-dimensionality of smart factories, it is not possible to predict all the possible events or prepare appropriate scenarios to respond. Reinforcement learning is an AI technique that provides the intelligent control processes needed to deal with such uncertainties. Due to the distributed nature of smart factories and the presence of multiple decision-making components, multi-agent reinforcement learning (MARL) should be incorporated instead of single-agent reinforcement learning (SARL), which, due to the complexities involved in the development process, has attracted less attention. In this research, we will review the literature on the applications of MARL to tasks within a smart factory and then demonstrate a mapping connecting smart factory attributes to the equivalent MARL features, based on which we suggest MARL to be one of the most effective approaches for implementing the control mechanism for smart factories

Multi-resolution forecast aggregation for time series in agri datasets

A wide variety of phenomena are characterized by time dependent dynamics that can be analyzed using time series methods. Various time series analysis techniques have been presented, each addressing certain aspects of the data. In time series analysis, forecasting is a challenging problem when attempting to estimate extended time horizons which effectively encapsulate multi-step-ahead (MSA) predictions. Two original solutions to MSA are the direct and the recursive approaches. Recent studies have mainly focused on combining previous methods as an attempt to overcome the problem of discarding sequential correlation in the direct strategy or accumulation of error in the recursive strategy. This paper introduces a technique known as Multi-Resolution Forecast Aggregation (MRFA) which incorporates an additional concept known as Resolutions of Impact. MRFA is shown to have favourable prediction capabilities in comparison to a number of state of the art methods

Anwendung von Reinforcement Learning in industriellen cyberphysischen Systemen

Die wachsende Besorgnis über den Klimawandel bewirkt, dass bei der Festlegung von Meilensteinen und Produktionsparametern für Fertigungssysteme ein größerer Fokus auf Energieeinsparung, Ressourcenschonung und Wandlungsfähigkeit gelegt wird. Dies ist auch notwendig, denn die Energiepreise steigen kontinuierlich an und durch den weltweiten Ausbruch von COVID-19 wurden die Lieferketten immer wieder gestört. Gleichzeitig hat die hohe Volatilität und Dynamik innerhalb der globalen Wertschöpfungsnetzwerke in letzter Zeit zu einer spürbaren Verkürzung der Produkt- und Technologiezyklen geführt. Darüber hinaus wandeln sich die Wünsche der Kunden, diese werden zunehmend anspruchsvoller und spezifischer. Um diesen Marktanforderungen gerecht zu werden, mussten und müssen Unternehmen ihre Fertigungsprozesse immer wieder anpassen und abwandeln. Ein wachsendes Portfolio und sinkende Auftragsvolumina (bis hin zur Losgröße 1) ergeben sich im Resultat. Um einen effektiven und effizienten Produktionsablauf zu realisieren, bedarf es ein dynamisches Regelwerk. Dieses bestimmt für die bestehenden Montagelinien und deren Fertigungszellen, unter Be- rücksichtigung des aktuellen Zustandes, auf welchen Stationen ein konkretes Produkt gefertigt werden soll. Gegenwärtig geschieht dies meist statisch über ein Manufacturing Execution System, welches für ganze Chargen entscheidet und üblicherweise nicht (oder nur schwerfällig) auf Unsicherheiten wie den Ausfall einer Operation, den Schwankungen in den Operationszeiten oder in der Qualität des Rohmaterials reagieren kann. Mit einem besonderen Fokus auf die Reduzierung der Gesamtproduktionszeit (und dem damit einhergehenden Energieverbrauch) eines Auftrags stellen wir in diesem Beitrag eine Simulationsumgebung vor, die eine Montagelinie des Industrial IoT Test Bed (an der HTW Dresden) nachbildet. Auf diese wenden wir vielversprechende Reinforcement Learning Methoden an, um für unterschiedliche Szenarien automatisiert eine kosteneffiziente Ressourcenzuweisung zu realisieren