Table Of Content

Ego autem et domus mea serviemus Domino. DEEP TIME SERIES FORECASTING With PYTHON An Intuitive Introduction to Deep Learn- ing for Applied Time Series Modeling Dr. N.D Lewis Copyright © 2016 by N.D. Lewis All rights reserved. No part of this publication may be reproduced, dis- tributed, or transmitted in any form or by any means, including photo- copying, recording, or other electronic or mechanical methods, without thepriorwrittenpermissionoftheauthor,exceptinthecaseofbriefquo- tationsembodiedincriticalreviewsandcertainothernoncommercialuses permitted by copyright law. For permission requests, contact the author at: www.AusCov.com. Disclaimer: Although the author and publisher have made every effort to ensure that the information in this book was correct at press time, the author and publisher do not assume and hereby disclaim any liability to anypartyforanyloss,damage,ordisruptioncausedbyerrorsoromissions, whether such errors or omissions result from negligence, accident, or any other cause. Ordering Information: Quantity sales. Special discounts are available on quantity purchases by corporations, associations, and others. For details, email: [email protected] Image photography by Deanna Lewis with helpful assistance from Naomi Lewis. ISBN-13: 978-1540809087 ISBN-10: 1540809080 Contents Acknowledgements iii Preface viii How to Get the Absolute Most Possible Benefit from this Book 1 GettingPython . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 LearningPython . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 UsingPackages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 5 1 The Characteristics of Time Series Data Simplified 7 UnderstandingtheDataGeneratingMechanism. . . . . . . . . . . . . . . . . 7 GeneratingaSimpleTimeSeriesusingPython . . . . . . . . . . . . . . . . . 9 RandomnessandReproducibility . . . . . . . . . . . . . . . . . . . . . . . . 12 TheImportanceofTemporalOrder. . . . . . . . . . . . . . . . . . . . . . . . 13 TheUltimateGoal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 ForAdditionalExploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2 Deep Neural Networks Explained 17 WhatisaNeuralNetwork? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 TheRoleofNeuron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 DeepLearninginaNutshell . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 GeneratingDataforusewithaDeepNeuralNetwork . . . . . . . . . . . . . 21 ExploringtheSampleData . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 TranslatingSampleDataintoaSuitableFormat . . . . . . . . . . . . . . . . 25 ASuperEasyDeepNeuralNetworkTool . . . . . . . . . . . . . . . . . . . . 26 AssessingModelPerformance . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 30 3 Deep Neural Networks for Time Series Forecasting the Easy Way 31 GettingtheDatafromtheInternet . . . . . . . . . . . . . . . . . . . . . . . 31 CleaningupDownloadedSpreadsheetFiles . . . . . . . . . . . . . . . . . . . 33 UnderstandingActivationFunctions . . . . . . . . . . . . . . . . . . . . . . . 36 HowtoScaletheInputattributes . . . . . . . . . . . . . . . . . . . . . . . . 39 AssessingPartialAutocorrelation . . . . . . . . . . . . . . . . . . . . . . . . . 42 ANeuralNetworkArchitectureforTimeSeriesForecasting . . . . . . . . . . 45 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 49 i 4 A Simple Way to Incorporate Additional Attributes in Your Model 51 WorkingwithAdditionalAttributes . . . . . . . . . . . . . . . . . . . . . . . 51 TheWorkingoftheNeuronSimplified . . . . . . . . . . . . . . . . . . . . . . 54 HowaNeuralNetworkLearns. . . . . . . . . . . . . . . . . . . . . . . . . . . 56 GradientDescentClarified. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 HowtoEasilySpecifyaModel . . . . . . . . . . . . . . . . . . . . . . . . . . 59 ChoosingaLearningRate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 TheEfficientWaytoRunYourModel . . . . . . . . . . . . . . . . . . . . . . 62 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 66 5 The Simple Recurrent Neural Network 67 WhyUseKeras? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 WhatisaRecurrentNeuralNetwork? . . . . . . . . . . . . . . . . . . . . . . 68 GainClarityontheRoleoftheDelayUnits . . . . . . . . . . . . . . . . . . . 71 FollowthisApproachtoCreateYourTrainandTestSets . . . . . . . . . . . 72 ParameterSharingClarified . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 UnderstandBackpropagationThroughTime . . . . . . . . . . . . . . . . . . 73 ACompleteIntuitiveGuidetoMomentum . . . . . . . . . . . . . . . . . . . 76 HowtoBenefitfromMiniBatching. . . . . . . . . . . . . . . . . . . . . . . . 78 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 81 6 Elman Neural Networks 83 PrepareYouDataforEasyUse . . . . . . . . . . . . . . . . . . . . . . . . . . 84 HowtoModelaComplexMathematicalRelationshipwithNoKnowledge . . 85 UsethisPythonLibraryforRapidResults. . . . . . . . . . . . . . . . . . . . 88 ExploringtheErrorSurface . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 ASuperSimpleWaytoFittheModel . . . . . . . . . . . . . . . . . . . . . . 91 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 93 7 Jordan Neural Networks 95 TheFastestPathtoDataPreparation . . . . . . . . . . . . . . . . . . . . . . 96 AStraightforwardModuleforJordanNeuralNetworks. . . . . . . . . . . . . 97 AssessingModelFitandPerformance . . . . . . . . . . . . . . . . . . . . . . 98 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 100 8 Nonlinear Auto-regressive Network with Exogenous Inputs 103 WhatisaNARXNetwork? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 SpreadsheetFilesMadeEasywithPandas . . . . . . . . . . . . . . . . . . . . 105 WorkingwithMacroeconomicVariables . . . . . . . . . . . . . . . . . . . . . 107 PythonandPandasDataTypes . . . . . . . . . . . . . . . . . . . . . . . . . 111 AToolforRapidNARXModelConstruction . . . . . . . . . . . . . . . . . . 113 HowtoRuntheModel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 117 9 Long Short-Term Memory Recurrent Neural Network 119 CyclicalPatternsinTimeSeriesData . . . . . . . . . . . . . . . . . . . . . . 119 WhatisanLSTM? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 EfficientlyExploreandQuicklyUnderstandData . . . . . . . . . . . . . . . . 123 TheLSTMMemoryBlockinaNutshell . . . . . . . . . . . . . . . . . . . . . 127 StraightforwardDataTransformationfortheTrainandTestSets . . . . . . . 128 ClarifytheRoleofGates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 UnderstandtheConstantErrorCarousel. . . . . . . . . . . . . . . . . . . . . 131 SpecifyingaLSTMModeltheEasyWay . . . . . . . . . . . . . . . . . . . . 132 ShufflingExamplestoImproveGeneralization . . . . . . . . . . . . . . . . . . 136 ANoteonVanishingGradients . . . . . . . . . . . . . . . . . . . . . . . . . . 138 FollowtheseStepstoBuildaStatefulLSTM . . . . . . . . . . . . . . . . . . 139 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 144 10Gated Recurrent Unit 145 TheGatedRecurrentUnitinaNutshell . . . . . . . . . . . . . . . . . . . . . 145 ASimpleApproachtoGatedRecurrentUnitConstruction . . . . . . . . . . 148 AQuickRecap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 HowtoUseMultipleTimeSteps . . . . . . . . . . . . . . . . . . . . . . . . . 151 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 154 11Forecasting Multiple Outputs 155 WorkingwithZippedFiles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 HowtoWorkwithMultipleTargets . . . . . . . . . . . . . . . . . . . . . . . 159 CreationofHandCraftedFeatures . . . . . . . . . . . . . . . . . . . . . . . . 161 ModelSpecificationandFit . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 166 12Strategies to Build Superior Models 169 RevisitingtheUKUnemploymentRateEconomicData . . . . . . . . . . . . 169 LimitationsoftheSigmoidActivationFunction . . . . . . . . . . . . . . . . . 171 OneActivationFunctionYouNeedtoAddtoYourDeepLearningToolkit. . 172 TryThisSimpleIdeatoEnhanceSuccess . . . . . . . . . . . . . . . . . . . . 176 ASimplePlanforEarlyStopping. . . . . . . . . . . . . . . . . . . . . . . . . 180 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 184 Index 189 Dedicated to Angela, wife, friend and mother extraordinaire.

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