Most our careers in the big enterprise involved working with time series. Sales, Shipments delivered, Advertising Dollars, marketing spends ... forecast plans and predictions. A favorite quote was 'the forecast is wrong', but how wrong? And Why? And what are the risks involved? So if we could do forecasts better, more data and intelligence based? How might we do it?
Using AutoML for Time Series Forecasting In the GoogleBlog.
Friday, December 4, 2020
Posted by Chen Liang and Yifeng Lu, Software Engineers, Google Research, Brain Team
Time series forecasting is an important research area for machine learning (ML), particularly where accurate forecasting is critical, including several industries such as retail, supply chain, energy, finance, etc. For example, in the consumer goods domain, improving the accuracy of demand forecasting by 10-20% can reduce inventory by 5% and increase revenue by 2-3%. Current ML-based forecasting solutions are usually built by experts and require significant manual effort, including model construction, feature engineering and hyper-parameter tuning. However, such expertise may not be broadly available, which can limit the benefits of applying ML towards time series forecasting challenges.
To address this, automated machine learning (AutoML) is an approach that makes ML more widely accessible by automating the process of creating ML models, and has recently accelerated both ML research and the application of ML to real-world problems. For example, the initial work on neural architecture search enabled breakthroughs in computer vision, such as NasNet, AmoebaNet, and EfficientNet, and in natural language processing, such as Evolved Transformer. More recently, AutoML has also been applied to tabular data.
Today we introduce a scalable end-to-end AutoML solution for time series forecasting, which meets three key criteria:
Today we introduce a scalable end-to-end AutoML solution for time series forecasting, which meets three key criteria:
Fully automated: The solution takes in data as input, and produces a servable TensorFlow model as output with no human intervention.
Generic: The solution works for most time series forecasting tasks and automatically searches for the best model configuration for each task.
High-quality: The produced models have competitive quality compared to those manually crafted for specific tasks.
We demonstrate the success of this approach through participation in the M5 forecasting competition, where this AutoML solution achieved competitive performance against hand-crafted models with moderate compute cost... .'
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