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SGM EA #LondonDataTribe meet up presentation 05032019
- 1. EA SPRINT’19 TIME SERIES (PART I) March 5TH 2019 Silvina Garrido – UKI Einstein Engagement Lead
- 2. AGENDA • Intro to Time Series • EA and Time Series • Demo Use Case • Demo • Summary • Q&A 2
- 3. • Time series forecasting is the use of a model to predict future values based on previously observed values in time. • EA Time Series/Dynamic Forecasting is a EA functionality that allow you to predict performance over the time. • In Part I today, with Einstein Analytics we will predict the future using Time Series in a declarative mode.Copyright © 2018 Accenture. All rights reserved. INTRODUCTION Time Series 3
- 4. Copyright © 2018 Accenture. All rights reserved. TIME SERIES Einstein Analytics Winter’19 Salesforce introduces “SAQL Time Series” result = timeseries resultSet generate (measure1 as fmeasure1 [, measure2 as fmeasure2...]) with (parameters); Sprint’19 Salesforce introduces “Time series function in compare-tables” and “Time Series Dashboard template”. • length (points) • dateCols (date) • IgnoreLast (true/false) • Order (optional) • partition • predictionInterva l • model • seasonality (0,4,12) • dateCols • points (length) • seasonality (0,4,12) • model • ignoreLast Compare Table Dashboard Template • Select Dimensions • dateCols • seasonality • predictionInterva l 4
- 5. Copyright © 2018 Accenture. All rights reserved. The PJM Interconnection Organization would like to predict the Electricity consumption in the next 12 months. Demo Use Case: Challenge: Create a Prediction in EA without coding SAQL. 5
- 6. DEMO
- 7. Copyright © 2018 Accenture. All rights reserved. 7 What were we able to build with declarative Time Series? • Time Series in Explorer Mode – Configure a Time Series in a Compare Table and visualize it in a time chart with Prediction marks. – Few limitations: Partitions and Prediction Interval. – Workaround: Use the Dashboard Template SUMMARY Let’s see what SAQL can do in Part II! • Time Series in Dashboard Mode – Configure a Dynamic Dashboard with a time Series Chart in less than a minute using the Time Serie Dashboard template. – Few Limitations: Ignore Last – simple modification on the SAQL ignoreLast=true and the Model (Auto, Additive and Multiplicative).
- 8. Q & A
- 9. THANK YOU!!! ESPECIAL THANKS TO BASHIR QAASIM
Editor's Notes
- Intro to Time Series Evolution of time series in Einstein – SAQL function in winter 19 NEW as config Spring 19 (1min) Intro to demo – Presenting the use case for a Government Agency - Energy Consumption (1min) Demo (12 min) – which includes to configure a compare table column with the Time Serie function and show a time series chart with the forecasted points. As this has the limitation of grouping the data, I will give another solution using a new Spring'19 feature - Dashboard Template - Time Serie. Showing the audience how to configure and explain the differences on the parameters. Q&A (4 min)
- •A Time series is a sequence taken at successive equally spaced points in time. •Time series analysis comprises methods for analysing time series data in order to extract meaningful statistics and other characteristics of the data. •Time series forecasting is the use of a model to predict future values based on previously observed values n time. Time series additive models: For monthly data, an additive model assumes that the difference between the January and July values is approximately the same each year. In other words, the amplitude of the seasonal effect is the same each year. Time series multiplicative models: in seasonal data, it might be more useful to model that the July value is the same proportion higher than the January value in each year, rather than assuming that their difference is constant. Assuming that the seasonal and other effects act proportionally on the series is equivalent to a multiplicative model
- length (required) Number of points to predict. For example, if length is 6 and the dateCols type string is Y-M, timeseries predicts data for 6 months. dateCols (optional) Date fields to use for grouping the data, plus the date column type string. For example, dateCols=(CloseDate_Year, CloseDate_Month, "Y-M"). Date columns are projected automatically. Allowed values are: YearField, MonthField, "Y-M” YearField, QuarterField, "Y-Q” YearField, "Y" ignoreLast (optional) If true, timeseries doesn't use the last time period in the calculations. The default is false. Set this parameter to true to improve the accuracy of the forecast if the last time period contains incomplete data. For example, if you are partway through the quarter, timeseries forecasts more accurately if you set this parameter to true. order (optional) Specify the field to use for ordering the data. Mandatory if dateCols is not used. By default, this field is sorted in ascending order. Use desc to specify descending order, for example order=('Type' desc). You can also order by multiple fields, for example order=('Type' desc, 'Group' asc). partition (optional) Specify the column used to partition the data. The column must be a dimension. The timeseries calculation is done separately for each partition to ensure that each partition uses the most accurate algorithm. For example, data in one partition might have a seasonal variation while data in another partition doesn't. The partition columns are projected automatically. predictionInterval (optional) Specify the uncertainty, or confidence interval, to display at each point. Allowed values are 80 and 95. The upper and lower bounds of the confidence interval are projected in columns named column_name_low_95 and column_name_high_95. model (optional) Specify which prediction model to use. If unspecified, timeseries calculates the prediction for each model and selects the best model using Bayesian information criterion (BIC). Allowed values are: None timeseries selects the best algorithm for the data Additive uses Holt's Linear Trend or Holt-Winters method with additive components. Multiplicative uses Holt's Linear Trend or Holt-Winters method with multiplicative components seasonality (optional) Specify the seasonality. Allowed values are: 0 No seasonality 4 Seasonality pattern that repeats every four periods. For example, if you set dateCols="Y-Q" then seasonality=4 specifies yearly seasonality, because four quarters equals one year. 12 Seasonality pattern that repeats every 12 periods. For example, if you set dateCols="Y-M" then seasonality=12 specifies yearly seasonality, because 12 months equals one year.