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Autoai for Time Series Forecasting AutoAI-TS: AutoAI for Time Series Forecasting Syed Yousaf Shah, Dhaval Patel, Long Vu, Xuan-Hong Dang, Bei Chen, Peter Kirchner, Horst Samulowitz, David Wood, Gregory Bramble, Wesley M. Gifford, Giridhar Ganapavarapu, Roman Vaculin, and Petros Zerfos IBM Thomas J. Watson Research Center Yorktown Heights, NY [email protected],[email protected],[email protected],[email protected] [email protected],[email protected],[email protected],[email protected],[email protected] [email protected],[email protected],[email protected],[email protected] ABSTRACT 2021. AutoAI-TS: AutoAI for Time Series Forecasting. In Proceedings of A large number of time series forecasting models including tra- ACM Conference (Conference’17). ACM, New York, NY, USA, 13 pages. https: ditional statistical models, machine learning models and more re- //doi.org/10.1145/nnnnnnn.nnnnnnn cently deep learning have been proposed in the literature. However, choosing the right model along with good parameter values that 1 INTRODUCTION performs well on a given data is still challenging. Automatically The task of constructing and tuning machine learning pipelines providing a good set of models to users for a given dataset saves [7, 21, 37] for a given problem and data set is a complex, man- both time and effort from using trial-and-error approaches with a ual, and labor-intensive endeavor that requires a diverse team of wide variety of available models along with parameter optimiza- data scientists and domain knowledge (subject matter) experts. It tion. We present AutoAI for Time Series Forecasting (AutoAI-TS) that is an iterative process that involves design choices and fine tun- provides users with a zero configurationzero-conf ( ) system to effi- ing of various stages of data processing and modeling, including ciently train, optimize and choose best forecasting model among data cleansing, data imputation, feature extraction and engineering, various classes of models for the given dataset. With its flexible model selection and parameter tuning. Automation of AI and Ma- zero-conf design, AutoAI-TS automatically performs all the data chine Learning [15, 31], usually referred to as AutoAI & AutoML, preparation, model creation, parameter optimization, training and aims to provide automated assistance for this complex process that model selection for users and provides a trained model that is ready requires highly specialized and hard-to-find data scientists and to use. For given data, AutoAI-TS utilizes a wide variety of models professionals. including classical statistical models, Machine Learning (ML) mod- AutoML has emerged as the natural evolution from earlier ef- els, statistical-ML hybrid models and deep learning models along forts on automated hyper-parameter optimization (HPO). It has with various transformations to create forecasting pipelines. It then gained increased popularity in recent years, including commercial evaluates and ranks pipelines using the proposed T-Daub mecha- offerings by the major cloud service providers12 [ , 19] and lead- nism to choose the best pipeline. The paper describe in detail all the ing open source ML libraries [13]. However, the overwhelming technical aspects of AutoAI-TS along with extensive benchmarking amount of work, including on benchmarking frameworks and state- on a variety of real world data sets for various use-cases. Bench- of-the-art comparisons [38], has focused so far on the classification mark results show that AutoAI-TS, with no manual configuration task. We believe that it’s due time to recalibrate the attention of from the user, automatically trains and selects pipelines that on AutoAI/AutoML research efforts to the equally important domain average outperform existing state-of-the-art time series forecasting of time series analysis, starting with the ever popular task of time toolkits. series forecasting. Automating the model selection and tuning of time series fore- KEYWORDS arXiv:2102.12347v2 [cs.LG] 8 Mar 2021 casting algorithms has been extensively studied in the context of machine learning; ML pipelines; AutoML; time series; optimization model selection within individual classes of models such as auto- ACM Reference Format: regressive/ARIMA [34] with the Box-Jenkins methodology, expo- Syed Yousaf Shah, Dhaval Patel, Long Vu, Xuan-Hong Dang, Bei Chen, nential smoothing models with the state-space methodology [16], Peter Kirchner, Horst Samulowitz, David Wood, Gregory Bramble, Wesley as well a newer efforts such as DeepAR [10] that augment deep M. Gifford, Giridhar Ganapavarapu, Roman Vaculin, and Petros Zerfos. neural networks and auto-regressive capabilities. However, due to the variety and heterogeneity of stochastic processes that generate Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed time series data, stemming from diverse domains such as Internet- for profit or commercial advantage and that copies bear this notice and the full citation of-Things sensor measurements, financial data series, cloud appli- on the first page. Copyrights for components of this work owned by others than ACM cation and service monitoring data to name a few, as well as the must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a proliferation of time series forecasting methods [26], ranging from fee. Request permissions from [email protected]. the classical statistical time series models, to ML-based techniques, Conference’17, July 2017, Washington, DC, USA to deep neural network-based ones [23], there is an ever expanding © 2021 Association for Computing Machinery. ACM ISBN 978-x-xxxx-xxxx-x/YY/MM...$15.00 need to automate pipeline selection and tuning across model classes https://doi.org/10.1145/nnnnnnn.nnnnnnn and approaches. The work in this paper aims to cover such need Conference’17, July 2017, Washington, DC, USA S. Yousaf Shah et al. through a zero configurationzero-conf ( ) system framework that en- the AutoAI-TS system architecture. Section 5 elaborates on our compasses many of the aforementioned approaches. Interestingly, extensive experiments and the performance evaluation, with details and as we further validated during the experimentation efforts of on datasets and results, including comparison to other state-of-the- our system, no single time series forecasting technique from those art systems. Section 6 concludes this paper. tested is able to accurately capture the complex patterns that are present in time series data from all domains that were analyzed. We present the design, implementation and performance evalua- 2 BACKGROUND AND RELATED WORK tion of a system called AutoAI for Time Series Forecasting (AutoAI-TS) AutoAI TS provides an end-to-end solution starting from the input for the automation of time series forecasting process using a vari- time series to the deployment of the top performing prediction ety of models. The goal of AutoAI-TS is to be a zero-conf system, pipelines. The development of fully automated time series fore- wherein the user simply drops-in their data set and the system casting tools has been increasingly attracting attention in recent transparently performs all the complex tasks of feature engineer- years due to their wide application across industries, e.g., finan- ing, training, parameter tuning, model ranking and returns one or cial services, retail, and manufacturing. There are a number open more of the best performing trained models ready for prediction. A source software tools proposed in literature ([18], [25], [35], [13]) single set of scikit-learn [8] compatible APIs provides a unified view and commercial products (e.g, DataRobot, Oneclick, Azure Time of the AutoAI-TS system to the end users contributing to ease of Series Insights, Element AI). Some of the early works rely use and familiarity of interfaces. Our goal is to design and develop on the selection of a single statistical model. For example, Hynd- a system that is able to achieve high accuracy on the time series man and his team have developed a set of algorithms for fitting forecasting task with minimal to no manual user input. ARIMA and extended exponential smoothing methods for auto- Our architecture incorporates various classes of time series fore- matic selection of the model orders and parameters [16, 18]. These casting models incorporated from open-source domain as well as methods are available via open source R package forecast and set of models developed in-house with scikit-learn compatible APIs. Python library Pyramid. Prophet from Facebook automates the The system also utilizes data transformations along with forecasting time series forecasting based on the Generalized Additive Model model to create pipelines and evaluate their feasibility for the given [14]. The fundamental algorithm assumes the underlying data can data. We further tailored hyper-parameter optimization techniques be systematically decomposed into trend, seasonality and causal for each class of models included in our system and introduced effects [35]. Prophet targets big data applications and is available a new technique for automatic (dynamic) extraction of optimal both in R and Python. In recent literature the efforts are more fo- look-back window length for ML models, based on input data. The cused on automating machine learning (ML) and deep learning list below
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