Time Series Forecasting Using Cyclic Boosting
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Generate accurate forecasts to understand how each prediction has been made.
Machine learning for time series forecasting achieved significant successes during the last few years; machine learning methods dominated the leaderboard in the M5 Kaggle Walmart forecasting competition.
And when I say machine learning, I mean precisely this — machine learning, not deep learning. As someone who has not only witnessed deep learning forecasting systems built by less savvy data science teams blowing in production but had to fix it successfully, I will never tire of saying that ‘𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐬 𝐖𝐡𝐚𝐭 𝐘𝐨𝐮 𝐃𝐨 𝐍𝐨𝐭 𝐍𝐞𝐞𝐝’.
If you want to learn why deep learning is not the answer to time series forecasting, please read my Medium article ‘𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐬 𝐖𝐡𝐚𝐭 𝐘𝐨𝐮 𝐃𝐨 𝐍𝐨𝐭 𝐍𝐞𝐞𝐝’. You can thank me later for not straying on the wrong path and saving your company a lot of effort and money.
Many powerful machine learning methods can deliver superior forecasting performance (follow me on LinkedIn and Twitter as I often highlight new SOTA developments in time series forecasting), with both ensembles and boosted trees often surpassing other methods. However, using complex machine learning and ensemble methods results in black box models, where it is difficult to understand the path leading to individual predictions.
To address this issue, BlueYonder research has published a novel ”CyclicBoosting” machine learning algorithm. CyclicBoosting is a generic supervised machine learning model performing accurate regression and classification tasks efficiently. At the same time, CyclicBoosting (CB) allows for a detailed understanding of how each prediction was made.
Such understanding is precious in situations where stakeholders would like to know how individual predictions were made and which factors contributed to them. Understanding how individual predictions were made can also be a regulatory requirement in many industries, such as health, finance, and insurance and desirable in other industries, such as manufacturing or retail.
In addition, many machine learning algorithms struggle to learn rare events, as such events are not representative of most of the data…
