OML feature highlight: Time Series analysis with Oracle Machine Learning

On this weekly Office Hours for Oracle Machine Learning on Autonomous Database, Bhoopendra Singh, Implementation Specialist - DWH, Lake, Analytics, Data Science and AI presented the concepts about the Time Series algorithms available in Oracle Database, and will do a Live Demo on OML Notebooks.

The Oracle Machine Learning product family supports data scientists, analysts, developers, and IT to achieve data science project goals faster while taking full advantage of the Oracle platform.

The Oracle Machine Learning Notebooks offers an easy-to-use, interactive, multi-user, collaborative interface based on Apache Zeppelin notebook technology, and support SQL, PL/SQL, Python and Markdown interpreters. It is available on all Autonomous Database versions and Tiers, including the always-free editions.

OML includes AutoML, which provides automated machine learning algorithm features for algorithm selection, feature selection and model tuning, in addition to a specialized AutoML UI exclusive to the Autonomous Database.

OML Services is also included in Autonomous Database, where you can deploy and manage native in-database OML models as well as ONNX ML models (for classification and regression) built using third-party engines, and can also invoke cognitive text analytics.

Transcript

1. OML feature highlight: Time Series analysis with Oracle Machine Learning OML Office Hours Bhoopendra Singh Implementation Specialist - DWH, Lake, Analytics, Data Science and AI Supported by Marcos Arancibia, Mark Hornick and Sherry LaMonica Product Management, Oracle Machine Learning Move the Algorithms; Not the Data! Copyright © 2021, Oracle and/or its affiliates. This Session will be Recorded
2. • Upcoming Sessions • Time Series analysis with Oracle Machine Learning • Q&A Topics for today Copyright © 2021, Oracle and/or its affiliates 2
3. November 16 2021 08:00 AM Pacific • Weekly Office Hours: OML on Autonomous Database - Ask & Learn November 9 2021 08:00 AM Pacific • OML Usage Highlight: Leveraging OML algorithms in Retail Science platform – Fraud Detection • Oracle Retail XBRi Loss Prevention solution flags outlier cashier behavior or outlier customer behavior using OML models, and can deliver the information to the retailer for further investigation. Upcoming Sessions Copyright © 2021, Oracle and/or its affiliates 3
4. ORACLE MACHINE LEARNING TIME SERIES FORECASTING Bhoopendra Singh Implementation Specialist Lift Services Analytics & Machine Learning
5. Agenda 5 Copyright © 2020, Oracle and/or its affiliates § Time Series § Time Series Patterns § Exponential Smoothing § Demo § Q/A
6. Time Series Copyright © 2020, Oracle and/or its affiliates 6 Time series forecasting is a technique for the prediction of events through a sequence of time. The techniques predict future events by analyzing the trends of the past, on the assumption that future trends will hold similar to historical trends . It is a specialized form of Regression, known in the literature as auto-regressive modeling. In OML • The input to time series analysis is a sequence of target values. A case id column specifies the order of the sequence. • The time series model provide estimates of the target value for each step of a time window that can include up to 30 steps beyond the historical data. • Oracle Machine Learning provides Exponential Smoothing Algorithm for predicting Time series Models. • Oracle time series analysis handles irregular time series.
7. Time Series Patterns Copyright © 2020, Oracle and/or its affiliates 7 Trend : A trend exists when there is a long-term increase or decrease in the data. It does not have to be linear. Seasonality: A seasonal pattern occurs when a time series is affected by seasonal factors such as the time of the year or the day of the Week. Cyclic : A cycle occurs when the data exhibit rises and falls that are not of a fixed frequency. Noise : Noise is the completely random fluctuation present in the data and we cannot use this component to forecast into the future
8. Exponential Smoothing Copyright © 2020, Oracle and/or its affiliates 8 Exponential Smoothing is a moving average method with a single parameter which models an exponentially decreasing effect of past levels on future values. Exponential Smoothing is extended to the following: • A matrix of models that mix and match error type (additive or multiplicative), trend (additive, multiplicative, or none), and seasonality (additive, multiplicative, or none) • Models with damped trends. • Models that directly handle irregular time series and time series with missing values.
9. Exponential Smoothing Copyright © 2020, Oracle and/or its affiliates 9 Simple Exponential Smoothing Simple Exponential Smoothing assumes the data fluctuates around a stationary mean, with no trend or seasonal pattern. In simple exponential smoothing model, each forecast (smoothed value) is computed as the weighted average of the previous observations, where the weights decrease exponentially depending on the value of smoothing constant α.
10. Exponential Smoothing Copyright © 2020, Oracle and/or its affiliates 10 Models with Trend but No Seasonality The preferred form of additive (linear) trend is sometimes called Holt’s method or double exponential smoothing. Models with trend add a smoothing parameter γ and optionally a damping parameter φ. The damping parameter smoothly dampens the influence of past linear trend on future estimates of level, often improving accuracy.
11. Exponential Smoothing Copyright © 2020, Oracle and/or its affiliates 11 Models with Seasonality but No Trend When the time series average does not change over time (stationary), but is subject to seasonal fluctuations, the appropriate model has seasonal parameters but no trend. Seasonal fluctuations are assumed to balance out over periods of length m, where m is the number of seasons, For example, m=4 might be used when the input data are aggregated quarterly
12. Exponential Smoothing Copyright © 2020, Oracle and/or its affiliates 12 Models with Trend and Seasonality Holt and Winters introduced both trend and seasonality in Exponential Smoothing Model(ESM). The original model, also known as Holt-Winters or triple exponential smoothing, considered an additive trend and multiplicative seasonality. Extensions include models with various combinations of additive and multiplicative trend, seasonality and error, with and without trend damping.
13. Demo: Forecasting Sales Using Exponential Smoothing Algorithm for Time Series Data Copyright © 2020, Oracle and/or its affiliates 13
14. Q & A Copyright © 2021, Oracle and/or its affiliates 14
15. Thank you Boopendra.singh@oracle.com Copyright © 2021, Oracle and/or its affiliates. 15