AWS Forcecast: DeepAR Predictor Time-series
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This document discusses time series forecasting methods and the AWS Forecast service. It provides an overview of traditional statistical versus modern machine learning approaches for time series. It then focuses on the DeepAR algorithm within AWS Forecast, explaining that it is a multi-step, multivariate approach that shares information across time series to model non-linearities and interactions. Best practices for using DeepAR are outlined, and there is a reference to a demo of DeepAR on an electricity dataset.
AWS Forcecast: DeepAR Predictor Time-series
- 1. Amplifying OrganisationalIntelligence Intellify Pty Ltd IntellifyAI Intellify_AISydney Level 8 11York Street Sydney, NSW 2000 T. (02) 8089 4073 www.intellify.com.au Melbourne Level 28 303 Collins Street Melbourne,VIC 3000 T. (03) 9132 9846 info@intellify.com.au 20 Bridge Street AWS Forecast: DeepAR Predictor Time-series
- 2. Amplifying OrganisationalIntelligence Agenda 1. Introduction 2. Why time-series and how are they different? 3. Classical (Statistical) and Modern time-series methods 4. AWS Forecast: Modern Time-series 5. AWS Predictor: DeepAR 6. Demo on DeepAR
- 3. Amplifying OrganisationalIntelligence Why are time-series methods important? 1 2 3 Time series are everywhere! Most methods were designed for use on cross-sectional data We can drive better business outcomes through the use of time-series methods
- 4. Amplifying OrganisationalIntelligence How are time series problems different? • Different states in a time series can make the problem harder to model. • There could be multiple forecasting horizons; short, medium, long term. • Typically you care about the prediction as well as the confidence in the prediction. • Model testing and validation must be conducted in a different way to avoid data leakage and select the best model.
- 5. Amplifying OrganisationalIntelligence What are desirable properties of time series methods? Multi-step multivariate prediction Shares information across time-series Leverages meta-information Works on sparse data Handles non-linearities/interactions Works with high dimensional data Models autocorrelation structure implicitly Minimal feature pre-processing and engineering
- 6. Amplifying OrganisationalIntelligence Traditional Models Autoregressive models are remarkably flexible at handling a wide range of different time series patterns, but … How about ability to learn and generalized from similar series (to learn more complex models without overfitting) Benefits Challenges • Interpretable • Implicitly models auto- correlation structure • Works well when there is little exogenous information • Doesn’t share information across time-series • Forecasting a large number of individual or grouped time- series • Struggles with sparsity and special events Benefits Challenges • Shares information across time series • Uses meta-information • Models non-linearities as well as interactions • Some works with missing values • Struggles if little meta- information • Requires larger volumes of data • Larger amounts of data preprocessing needed. • Tend to average predictions too much across time series Based on neural networks with a modified architecture. Implicitly models interactions, non-linearities as well as time-series features. LSTM’s (vs RNN) do a better job of modelling long term time dependencies. ML Models Random Forest. Prophet. LSTM. AWS ForecastAutoregressive ARIMA. ETS Classic vs Modern Models: Benefit & Challenges
- 7. Amplifying OrganisationalIntelligence Benefits • 50% more accurate forecasts with machine learning • Reduce forecasting time from months to hours Use cases • Product Demand Planning • Retail product demand • Supply chain demand • Operational metrics • Business metrics • Financial planning • Resource planning Statistical Machine Learning Volume of data Works well with little information Needs data from several series or several features Can share meta- information No (ARIMAX exception) Yes Can handle sparse data No Yes Can handle non- linearities/interaction s No or only explicitly Yes Can leverage shared information between time-series No (VAR exception) Yes, but tends to average too much Can work with high dimensional data Limited Yes AWS Forecast Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts.
- 8. Amplifying OrganisationalIntelligence Datasets and Dataset Groups Predictors Forecasts AWS Forecast HowThis Works? Setting Up: • Sign Up for AWS • Set Up the AWSCLI • Set Up Permissions for Amazon Forecast • Autoregressive Integrated Moving Average(ARIMA) • arn:aws:forecast:::algorithm/ARIMA • DeepAR • arn:aws:forecast:::algorithm/Deep_AR • Prophet • arn:aws:forecast:::algorithm/Prophet Predictor:
- 9. Amplifying OrganisationalIntelligence AWS Predictor: DeepAr DeepAR is a forecasting model based on autoregressive RNNs, which learns a global model from historical data of all time series in all datasets DeepAr is Multi-step multivariate time series: • Given observed values of a series i for t time-steps, estimating probability distribution of the next T steps Pros Cons • Shares information across groups of time series • Models non-linearities as well as interactions • Minimal manual feature engineering • Ability to incorporate a wide range of likelihood models, including probabilistic forecasts in the form of Monte Carlo samples • Struggles if little meta- information • Requires larger volumes of data • Tend to average predictions too much across time series
- 10. Amplifying OrganisationalIntelligence Best Practices for using the DeepAR Algorithm • Input/Output interface: • Supports two data channels (Train and Test for evaluation) • Format: JSON, gzip, and Parquet • Best practice: • Except for when splitting your dataset for train and test, always provide the entire time series. Why: the lagged value features • Test points should start immediately after the last time point of training • Avoid using very large values (>400) for the prediction length because it makes the model slow and less accurate. Solution: consider aggregating your data at a higher frequency. • ARIMA or ETS, might provide more accurate results on on a single time series. The DeepAR algorithm starts to outperform the standard methods when your dataset contains hundreds of related time series. • Train: on both GPU and CPU instances. Inference: only CPU • Use small number for context_length, prediction_length, num_cells, num_layers, or mini_batch_size, in case of small instances
- 11. Amplifying OrganisationalIntelligence DeepAR: Demo SageMaker/DeepAR demo on electricity dataset
- 12. Amplifying OrganisationalIntelligence References • https://docs.aws.amazon.com/forecast/latest/dg/forecast.dg.pdf • https://aws.amazon.com/blogs/aws/amazon-forecast-time-series-forecasting-made-easy/ • https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html
Editor's Notes
- #4: Lets first start with why time-series series methods are important. The first reasons is that time series problems are everywhere; they appear in financial data, customer behavior data, property data and engineering problems. In fact, in our experience, we have that around 70% of our consulting projects have some time-series component or consideration that needs to be incorporated into the solution. The second reason is that most methods, especially the standard ones inside statistics and machine learning are built for cross-sectional problems. If you haven’t heard of this terminology before, cross sectional problems are where we take many observations at a point in time from many individuals.
- #5: Time–series data is a chronological sequence of observations on a particular variable.
- #6: Time–series data is a chronological sequence of observations on a particular variable.
- #7: Exponential smoothing (ETS methods) Classical methods typically work through: Decomposition of time-series into each of its components Find average historical affects for each component Aggregate average historical affects and forecast one step ahead Modern time series methods follow the same patterns as traditional machine learning approaches with 3 major modifications: Time-series features are manually created by the user (time-series feature engineering) if the algorithm cannot implicitly model them. Specific Machine learning methods are applied that give us the point estimate as well as the distribution. Traditional time series validation (not random sampling) is used with specific metrics.
- #8: Developers with no machine learning expertise can use the Amazon Forecast APIs, AWS Command Line Interface (AWS CLI), or Amazon Forecast console to import training data into one or more Amazon Forecast datasets, train predictors, and generate forecasts.
- #9: When creating forecasting projects in Amazon Forecast, you work with the following resources: Before using Amazon Forecast to evaluate or forecast time-series data, create an AWS account, configure access permissions, and set up the AWS Command Line Interface (AWS CLI).
- #10: Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence
- #12: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/deepar_electricity/DeepAR-Electricity.ipynb