Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. 
We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Forecasting: theory and practice

The basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and not user-friendly, which is one of the reasons blockchain technology is not fully accepted. In this paper, we propose a machine learning-based method, which introduces automated signing of blockchain transactions, while including also a personalized identification of anomalous transactions. In order to evaluate the proposed method, an experiment and analysis were performed on data from the Ethereum public main network. The analysis shows promising results and paves the road for a possible future integration of such a method in dedicated digital signing software for blockchain transactions.

https://www.mdpi.com/1424-8220/20/1/147/pdf

A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection

Data-driven identification and model predictive control of biomass gasification process for maximum energy production

Using multiple time series analysis for geosensor data forecasting

Research on the long-term and short-term forecasts of navigable river’s water-level fluctuation based on the adaptive multilayer perceptron

Prediction of time series data is an important application in many domains. Despite their advantages, traditional databases and MapReduce methodology are not ideally suited for this type of processing due to dependencies introduced by the sequential nature of time series. We present a novel framework to facilitate retrieval and rolling-window prediction of irregularly sampled large-scale time series data. By introducing a new index pool data structure, processing of time series can be efficiently parallelised. The proposed framework is implemented in R programming environment and utilises Hadoop to support parallelisation and fault tolerance. Experimental results indicate our proposed framework scales linearly up to 32-nodes.

/pdf/rolling-window-time-series-prediction-using-mapreduce-4otlntclhw.pdf

Rolling window time series prediction using MapReduce

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Rolling window time series prediction using MapReduce