Combining forecasts: Performance and coherence

Thomson, Mary E.; Pollock, Andrew C.; Önkal, Dilek; Gönül, M. Sinan

doi:10.1016/j.ijforecast.2018.10.006

Cited by 43 publications

(28 citation statements)

References 39 publications

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“…Later it will be forecasted through Holt's Winter forecasting method, errors will be calculated, and the results obtained from both forecasting methods will be tailored in the model equation ( 6) mentioned below. Higher volatility or inaccuracy in forecasting warrants the utilisation of consolidated or combined forecasting model (THOMSON et al 2019;ZHANG and ZHANG 2018). The manner of thinking around weighted consolidated forecasting, otherwise called composite forecasting, is not new (ISLEK, S. G. O 2017).…”

Section: Arhow Modelmentioning

confidence: 99%

“…Based on the consolidated forecasting: 80% is accounted for from CHAI, and 20% are linear extrapolation. Deep learning tools can help achieve higher forecast accuracy by creating patterns in decision making rather than using a single model to forecast demand (THOMSON et al 2019). By combining various machine-learning models; it will help to resolve issues of warehouse forecasting.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

Siddiqui

Azmat

Ahmed

et al. 2021

Supply Chain Forum: An International Journal

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In the era of modern technology, the competitive paradigm among organisations is changing at an unprecedented rate. New success measures are applied to the organisation's supply chain performance to outperform the competition. However, this lead can only be obtained and sustained if the organisation has an effective and efficient supply chain and an appropriate forecasting technique. Thus, this study presents the demand-forecasting model, i.e., a good fit for the pharmaceutical sector, and shows promising results. Through this study, it is observed that combining forecasting algorithms can result in greater forecasting accuracies. Therefore, a combined forecasting technique ARIMA-HW hybrid 1 i.e. (ARHOW) combines the Autoregressive Integrated Moving Average and Holt' s-Winter model. The empirical findings confirm that ARHOW performs better than widely used forecasting techniques ARIMA, Holts Winter, ETS and Theta. The results of the study indicate that pharmaceutical companies can adopt this model for improved demand forecasting.

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Section: Arhow Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

Siddiqui

Azmat

Ahmed

et al. 2021

Supply Chain Forum: An International Journal

View full text Add to dashboard Cite

show abstract

“…As such, instead of considering selecting a single model (per series or across series), combinations of forecasts is an alternative way forward. Forecast combinations tend to yield better results when one is averaging forecasts from robust models but also when there is a significant diversity among the forecasts (Wang and Petropoulos, 2016;Thomson et al, 2019;Lichtendahl and Winkler, 2020). Combinations of forecasts address two of the sources of uncertainty in forecasting (Petropoulos et al, 2018), model's form and model's parameters uncertainty, as we do not rely on a single model anymore.…”

Section: Background Researchmentioning

confidence: 99%

Improving forecasting by subsampling seasonal time series

Li¹,

Petropoulos²,

Kang³

2021

Preprint

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Time series forecasting plays an increasingly important role in modern business decisions.In today's data-rich environment, people often aim to choose the optimal forecasting model for their data. However, identifying the optimal model often requires professional knowledge and experience, making accurate forecasting a challenging task. To mitigate the importance of model selection, we propose a simple and reliable algorithm and successfully improve the forecasting performance. Specifically, we construct multiple time series with different sub-seasons from the original time series. These derived series highlight different sub-seasonal patterns of the original series, making it possible for the forecasting methods to capture diverse patterns and components of the data. Subsequently, we make forecasts for these multiple series separately with classical statistical models (ETS or ARIMA). Finally, the forecasts of these multiple series are combined with equal weights. We evaluate our approach on the widely-used forecasting competition datasets (M1, M3, and M4), in terms of both point forecasts and prediction intervals. We observe improvements in performance compared with the benchmarks. Our approach is particularly suitable and robust for the datasets with higher frequencies. To demonstrate the practical value of our proposition, we showcase the performance improvements from our approach on hourly load data.

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“…In such settings, it is often required to obtain good forecasts, not just for the leaf level time-series (fine grained forecasts), but also for the aggregated time-series corresponding to higher level nodes (coarse gained forecasts). Furthermore, for interpretability and business decision making purposes, it is often desirable to obtain predictions that are roughly coherent or consistent (Thomson et al, 2019) with respect to the hierarchy tree. This means that the predictions for each parent time-series is equal to the sum of the predictions for its children time-series.…”

Section: Introductionmentioning

confidence: 99%

Hierarchically Regularized Deep Forecasting

Paria,

Sen,

Ahmed

et al. 2021

Preprint

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Hierarchical forecasting is a key problem in many practical multivariate forecasting applications -the goal is to simultaneously predict a large number of correlated time series that are arranged in a pre-specified aggregation hierarchy. The challenge is to exploit the hierarchical correlations to simultaneously obtain good prediction accuracy for time series at different levels of the hierarchy. In this paper, we propose a new approach for hierarchical forecasting based on decomposing the time series along a global set of basis time series and modeling hierarchical constraints using the coefficients of the basis decomposition for each time series. Unlike past methods, our approach is scalable at inference-time (forecasting for a specific time series only needs access to its own data) while (approximately) preserving coherence among the time series forecasts. We experiment on several publicly available datasets and demonstrate significantly improved overall performance on forecasts at different levels of the hierarchy, compared to existing state-of-the-art hierarchical reconciliation methods. * Part of this work was done while BP was an intern at Google. Preprint. Under review.

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Combining forecasts: Performance and coherence

Cited by 43 publications

References 39 publications

A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

Improving forecasting by subsampling seasonal time series

Hierarchically Regularized Deep Forecasting

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