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We develop a time series model to forecast weekly peak power demand for three main states of Australia for a yearly timescale, and show the crucial role of environmental factors in improving the forecasts. More precisely, we construct a seasonal autoregressive integrated moving average (SARIMA) model and reinforce it by employing the exogenous environmental variables including, maximum temperature, minimum temperature, and solar exposure. The estimated hybrid SARIMA-regression model exhibits an excellent mean absolute percentage error (MAPE) of 3.41 % . Moreover, our analysis demonstrates the importance of the environmental factors by showing a remarkable improvement of 46.3 % in MAPE for the hybrid model over the crude SARIMA model which merely includes the power demand variables. In order to illustrate the efficacy of our model, we compare our outcome with the state-of-the-art machine learning methods in forecasting. The results reveal that our model outperforms the latter approach. [ABSTRACT FROM AUTHOR]

With large quantities of data typically available nowadays, forecasting models that are trained across sets of time series, known as Global Forecasting Models (GFM), are regularly outperforming traditional univariate forecasting models that work on isolated series. As GFMs usually share the same set of parameters across all time series, they often have the problem of not being localised enough to a particular series, especially in situations where datasets are heterogeneous. We study how ensembling techniques can be used with generic GFMs and univariate models to solve this issue. Our work systematises and compares relevant current approaches, namely clustering series and training separate submodels per cluster, the so-called ensemble of specialists approach, and building heterogeneous ensembles of global and local models. We fill some gaps in the existing GFM localisation approaches, in particular by incorporating varied clustering techniques such as feature-based clustering, distance-based clustering and random clustering, and generalise them to use different underlying GFM model types. We then propose a new methodology of clustered ensembles where we train multiple GFMs on different clusters of series, obtained by changing the number of clusters and cluster seeds. Using Feed-forward Neural Networks, Recurrent Neural Networks, and Pooled Regression models as the underlying GFMs, in our evaluation on eight publicly available datasets, the proposed models are able to achieve significantly higher accuracy than baseline GFM models and univariate forecasting methods. [ABSTRACT FROM AUTHOR]

• Time series data augmentation techniques for global forecasting models. • Importing time series domain knowledge using pooled and transfer learning approaches. • Improving the accuracy of baseline global forecasting models. Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques. In most cases, GFMs are implemented using deep neural networks, and in particular Recurrent Neural Networks (RNN), which require a sufficient amount of time series to estimate their numerous model parameters. However, many time series databases have only a limited number of time series. In this study, we propose a novel, data augmentation based forecasting framework that is capable of improving the baseline accuracy of the GFM models in less data-abundant settings. We use three time series augmentation techniques: GRATIS, moving block bootstrap (MBB), and dynamic time warping barycentric averaging (DBA) to synthetically generate a collection of time series. The knowledge acquired from these augmented time series is then transferred to the original dataset using two different approaches: the pooled approach and the transfer learning approach. When building GFMs, in the pooled approach, we train a model on the augmented time series alongside the original time series dataset, whereas in the transfer learning approach, we adapt a pre-trained model to the new dataset. In our evaluation on competition and real-world time series datasets, our proposed variants can significantly improve the baseline accuracy of GFM models and outperform state-of-the-art univariate forecasting methods. [ABSTRACT FROM AUTHOR]

• Building a model on a set of related time series can improve the forecast accuracy. • Performance of the global models can degenerate if built on disparate time series. • A subgrouping strategy then augments the accuracies of the baseline global models. With the advent of Big Data, nowadays in many applications databases containing large quantities of similar time series are available. Forecasting time series in these domains with traditional univariate forecasting procedures leaves great potentials for producing accurate forecasts untapped. Recurrent neural networks (RNNs), and in particular Long Short Term Memory (LSTM) networks, have proven recently that they are able to outperform state-of-the-art univariate time series forecasting methods in this context, when trained across all available time series. However, if the time series database is heterogeneous, accuracy may degenerate, so that on the way towards fully automatic forecasting methods in this space, a notion of similarity between the time series needs to be built into the methods. To this end, we present a prediction model that can be used with different types of RNN models on subgroups of similar time series, which are identified by time series clustering techniques. We assess our proposed methodology using LSTM networks, a widely popular RNN variant, together with various clustering algorithms, such as kMeans, DBScan, Partition Around Medoids (PAM), and Snob. Our method achieves competitive results on benchmarking datasets under competition evaluation procedures. In particular, in terms of mean sMAPE accuracy it consistently outperforms the baseline LSTM model, and outperforms all other methods on the CIF2016 forecasting competition dataset. [ABSTRACT FROM AUTHOR]

• Time series simulation using Data Generating Processes. • Simulating a number of forecasting scenarios close to real-world situations. • Evaluating the conditions when global forecasting models are competitive. The recent advances in Big Data have opened up the opportunity to develop competitive Global Forecasting Models (GFM) that simultaneously learn from many time series. Although, the concept of series relatedness has been heavily exploited with GFMs to explain their superiority over local statistical benchmarks, this concept remains largely under-investigated in an empirical setting. Hence, this study attempts to explore the factors that affect GFM performance, by simulating a number of datasets having controllable characteristics. The factors being controlled are along the homogeneity/heterogeneity of series, the complexity of patterns in the series, the complexity of forecasting models, and the lengths/number of series. We simulate time series from simple Data Generating Processes (DGP), such as Auto Regressive (AR), Seasonal AR and Fourier Terms to complex DGPs, such as Chaotic Logistic Map, Self-Exciting Threshold Auto-Regressive and Mackey-Glass Equations. We perform experiments on these datasets using Recurrent Neural Networks (RNN), Feed-Forward Neural Networks, Pooled Regression models and Light Gradient Boosting Models (LGBM) built as GFMs, and compare their performance against standard statistical forecasting techniques. Our experiments demonstrate that with respect to GFM performance, relatedness is closely associated with other factors such as the availability of data, complexity of data and the complexity of the forecasting technique used. Also, techniques such as RNNs and LGBMs having complex non-linear modelling capabilities, when built as GFMs are competitive methods under challenging forecasting scenarios such as short series, heterogeneous series and having minimal prior knowledge of the data patterns. [ABSTRACT FROM AUTHOR]