Table Of Content

NEURAL NETWORKS FOR TIME SERIES FORECASTING With R Intuitive Step by Step Blueprint for Beginners Dr. N.D Lewis Copyright © 2017 by N.D. Lewis Ordering Information: Quantity sales. Special discounts are available on quantity purchases by corporations, associations, and others. For details, email: [email protected] ISBN-13: 978-1544752952 ISBN-10: 1544752954 Contents Preface viii How to Get the Absolute Most Possible Benefit from this Book 1 GettingR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 LearningR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 UsingPackages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 EffectiveUseofFunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 AdditionalResourcestoCheckOut. . . . . . . . . . . . . . . . . . . . . . . . 5 1 The Characteristics of Time Series Data 7 TimeSeriesinPractice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 BusinessandEconomicTimeSeries . . . . . . . . . . . . . . . . . . . . 7 HealthandMedicalTimeSeries. . . . . . . . . . . . . . . . . . . . . . . 9 PhysicalandEnvironmentalTimeSeries . . . . . . . . . . . . . . . . . . 10 TimeSeriesofCounts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 DiscreteTimeSeries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 UnderstandingtheDataGeneratingMechanism. . . . . . . . . . . . . . . . . 13 TimeSeriesDecomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 TheBenefitofNeuralNetworksforTimeSeriesModeling . . . . . . . . . . . 16 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2 Feed-Forward Neural Networks Explained 19 UnderstandingNeuralNetworks . . . . . . . . . . . . . . . . . . . . . . . . . 20 TheRoleofNeuron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 ExplainingActivationFunctions . . . . . . . . . . . . . . . . . . . . . . 22 TheWorkingoftheNeuronSimplified . . . . . . . . . . . . . . . . . . . 26 HowaNeuralNetworkLearns. . . . . . . . . . . . . . . . . . . . . . . . 27 GradientDescentClarified. . . . . . . . . . . . . . . . . . . . . . . . . . 29 ExploringtheErrorSurface . . . . . . . . . . . . . . . . . . . . . . . . . 31 ChoosingaLearningRate . . . . . . . . . . . . . . . . . . . . . . . . . . 33 ACompleteIntuitiveGuidetoMomentum . . . . . . . . . . . . . . . . 35 DeepNeuralNetworksinaNutshell . . . . . . . . . . . . . . . . . . . . 36 TheRoleofBatching. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 PracticalApplicationofFeed-ForwardNeuralNetworks . . . . . . . . . . . . 38 ForecastingStockPrices . . . . . . . . . . . . . . . . . . . . . . . . . . 38 PredictingCropYields . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 ModelingLakeWaterLevels. . . . . . . . . . . . . . . . . . . . . . . . . 41 Example-ForecastingtheNumberofNewCasesofEscherichiacoli . . . . . 42 Step1-CollectingData . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Step2-ExploringandPreparingtheData . . . . . . . . . . . . . . . . 44 Step3-TrainingaModelontheData . . . . . . . . . . . . . . . . . . 54 Step4-EvaluatingModelPerformance . . . . . . . . . . . . . . . . . . 56 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 57 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3 Recurrent Neural Networks 67 UnderstandingRecurrentNeuralNetworks. . . . . . . . . . . . . . . . . . . . 68 TheDifferencebetweeenDynamicandStaticNeuralNetworks . . . . . 68 AVisualExplanationofRecurrentNeuralNetworks . . . . . . . . . . . 68 AQuickLookatTuringcomplete. . . . . . . . . . . . . . . . . . . . . . 69 TheRoleoftheDelayUnits . . . . . . . . . . . . . . . . . . . . . . . . . 70 ParameterSharingClarified . . . . . . . . . . . . . . . . . . . . . . . . . 71 BackpropagationThroughTime . . . . . . . . . . . . . . . . . . . . . . 71 PracticalApplicationofRecurrentNeuralNetworks . . . . . . . . . . . . . . 72 ManagementofPhytoplanktonBiomass . . . . . . . . . . . . . . . . . . 72 RiverFlowForecasting. . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 ForecastingInternetRoundTripTime . . . . . . . . . . . . . . . . . . . 74 Example-ForecastingtheWholeBirdSpotPriceofChicken . . . . . . . . 75 Step1-CollectingData . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Step2-ExploringandPreparingtheData . . . . . . . . . . . . . . . . 76 Step3-TrainingaModelontheData . . . . . . . . . . . . . . . . . . 80 Step4-EvaluatingModelPerformance . . . . . . . . . . . . . . . . . . 82 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 84 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4 Long Short-Term Memory Recurrent Neural Network 93 UnderstandingLongShort-TermMemoryRecurrentNeuralNetworks . . . . 94 TheLSTMMemoryBlockinaNutshell . . . . . . . . . . . . . . . . . . 94 ClarifytheRoleofGates . . . . . . . . . . . . . . . . . . . . . . . . . . 96 UnderstandtheConstantErrorCarousel. . . . . . . . . . . . . . . . . . 97 ANoteonVanishingGradients . . . . . . . . . . . . . . . . . . . . . . . 98 PracticalApplicationofLongShort-TermMemoryRecurrentNeuralNetworks100 EffectiveMindControl . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 AnomalyDetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 MortalityRiskPredictions . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Example-PredictingEyeMovements . . . . . . . . . . . . . . . . . . . . . . 103 Step1-CollectingData . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Step2-ExploringandPreparingtheData . . . . . . . . . . . . . . . . 103 Step3-TrainingaModelontheData . . . . . . . . . . . . . . . . . . 108 Step4-EvaluatingModelPerformance . . . . . . . . . . . . . . . . . . 109 Step5-ViewingTrainingSetPerformance . . . . . . . . . . . . . . . . 111 Example-ForecastingElectrocardiogramActivity . . . . . . . . . . . . . . . 115 Step1-CollectingData . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Step2-ExploringandPreparingtheData . . . . . . . . . . . . . . . . 116 Step3-TrainingaModelontheData . . . . . . . . . . . . . . . . . . 119 Step4-EvaluatingModelPerformance . . . . . . . . . . . . . . . . . . 120 Step5-AssessingTestSetPerformance . . . . . . . . . . . . . . . . . 123 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 5 Gated Recurrent Unit Neural Networks 127 UnderstandingGatedRecurrentUnitNeuralNetworks . . . . . . . . . . . . 128 TheResetGate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 TheUpdateGate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 PracticalApplicationofGatedRecurrentUnitNeuralNetworks . . . . . . . 130 PatientStateofHealth. . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 AnomalyDetectioninAircraftTimeSeries . . . . . . . . . . . . . . . . 131 Example-PredictingEyeMovements . . . . . . . . . . . . . . . . . . . . . . 132 Step1-CollectingData . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Step2-ExploringandPreparingtheData . . . . . . . . . . . . . . . . 132 Step3-TrainingaModelontheData . . . . . . . . . . . . . . . . . . 133 Step4-EvaluatingModelPerformance . . . . . . . . . . . . . . . . . . 134 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 136 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 6 Elman Neural Networks 141 UnderstandingElmanNeuralNetworks . . . . . . . . . . . . . . . . . . . . . 141 RoleofMemory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 PracticalApplicationofElmanNeuralNetworks . . . . . . . . . . . . . . . . 143 ElectricityMarketPriceForecasting . . . . . . . . . . . . . . . . . . . . 143 CrudeOilFutures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 WeatherForecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 HowtoImmediatelyFindaSeriousFault . . . . . . . . . . . . . . . . . 146 AnInnovativeIdeaforBetterWater . . . . . . . . . . . . . . . . . . . . 147 HowtoMakeaKillingintheStockMarket . . . . . . . . . . . . . . . . 148 Example-ForecastingHistoricalWheatPrices . . . . . . . . . . . . . . . . . 148 Step1-CollectingData . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Step2-ExploringandPreparingtheData . . . . . . . . . . . . . . . . 150 Step3-TrainingaModelontheData . . . . . . . . . . . . . . . . . . 158 Step4-EvaluatingModelPerformance . . . . . . . . . . . . . . . . . . 159 Step5-AssessingTestSetPerformance . . . . . . . . . . . . . . . . . . 161 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 7 Jordan Neural Networks 165 UnderstandingJordanNeuralNetworks . . . . . . . . . . . . . . . . . . . . . 166 PracticalApplicationofJordanNeuralNetworks . . . . . . . . . . . . . . . . 167 WindSpeedForecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 TrafficFlowForecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 MonitoringStockMarketVolatility . . . . . . . . . . . . . . . . . . . . . 169 Example-ModelingAirTemperature . . . . . . . . . . . . . . . . . . . . . . 170 Step1-CollectingData . . . . . . . . . . . . . . . . . . . . . . . . . . 170 Step2-ExploringandPreparingtheData . . . . . . . . . . . . . . . . 170 Step3-TrainingaModelontheData . . . . . . . . . . . . . . . . . . 175 Step4-EvaluatingModelPerformance . . . . . . . . . . . . . . . . . . 175 Step5-AssessingTestSetPerformance . . . . . . . . . . . . . . . . . . 176 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 8 General Method of Data Handling Type Neural Networks 181 UnderstandingtheGeneralMethodofDataHandlingTypeNeuralNetworks 182 SomeAreasofOutstandingSuccess. . . . . . . . . . . . . . . . . . . . . 183 TheGeneralMethodofDataHandlinginaNutshell . . . . . . . . . . . 184 AnImportantRuleforDataScience . . . . . . . . . . . . . . . . . . . . 188 InductiveBias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 InductivevDeductiveInference . . . . . . . . . . . . . . . . . . . . . . . 192 AShortStory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 PracticalApplicationofSelf-OrganizingPolynomialNeuralNetworks. . . . . 198 CoalMiningAccidents . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 StockSpecificPriceForecasting . . . . . . . . . . . . . . . . . . . . . . . 199 Example-ForecastingMonthlySunspotNumbers . . . . . . . . . . . . . . . 200 Step1-CollectingData . . . . . . . . . . . . . . . . . . . . . . . . . . 200 Step2-ExploringandPreparingtheData . . . . . . . . . . . . . . . . 201 Step3-TrainingaModelontheData . . . . . . . . . . . . . . . . . . 203 Step4-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 205 Step5-CreatingOneMonthAheadForecasts . . . . . . . . . . . . . . 207 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Index 220 Finally, A Blueprint for Neural Net- work Time Series Forecasting with R! Neural Networks for Time Series Forecasting with R offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle, fun and unhurried journey to creating neural network models for time series forecasting with R. Whether you are new to data science or a veteran, this book offers a powerful set of tools for quickly and easily gaining insight from your data using R. NO EXPERIENCE REQUIRED: Neural Networks for Time Series Forecasting with R uses plain language rather than a ton of equations; I’m assuming you never did like linear algebra, don’t want to see things derived, dislike complicated computer code, and you’re here because you want to try neural networks for time series forecasting for yourself. YOURPERSONALBLUEPRINT:Throughasimpletofollow step by step process, you will learn how to build neuralnetwork timeseriesforecastingmodelsusingR.Onceyouhavemastered the process, it will be easy for you to translate your knowledge into your own powerful applications. THIS BOOK IS FOR YOU IF YOU WANT: • Explanations rather than mathematical derivation. • Practical illustrations that use real data. • Worked examples in R you can easily follow and immediately implement. • Ideas you can actually use and try out with your own data. TAKE THE SHORTCUT: Neural Networks for Time Se- ries Forecasting with R was written for people who want to get up to speed as quickly as possible. In this book you will learn how to: • UnleashthepowerofLongShort-TermMemoryNeuralNetworks. • Develop hands-on skills using the Gated Recurrent Unit Neural Network. • Design successful applications with Recurrent Neural Networks. • Deploy Jordan and Elman Partially Recurrent Neural Networks. • Adapt Deep Neural Networks for Time Series Forecasting. • Master the General Method of Data Handling Type Neural Net- works. QUICK AND EASY: For each neural network model, every step in the process is detailed, from preparing the data for analysis, to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks. Usingplainlanguage, thisbookoffersasimple, intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using R. GET STARTED TODAY! Everything you need to get started is contained within this book. Neural Networks for Time Series Forecasting with R is your very own hands-on prac- tical, tactical, easy to follow guide to mastery. Buy this book today and accelerate your progress! Other Books by N.D Lewis • Deep Learning Made Easy with R: – Volume I: A Gentle Introduction for Data Science – Volume II: Practical Tools for Data Science – Volume III: Breakthrough Techniques to Transform Performance • Deep Learning for Business with R • Build Your Own Neural Network TODAY! • 92 Applied Predictive Modeling Techniques in R • 100 Statistical Tests in R • Visualizing Complex Data Using R • Learning from Data Made Easy with R • Deep Time Series Forecasting with Python • Deep Learning for Business with Python • Deep Learning Step by Step with Python For further detail’s visit www.AusCov.com Preface If you are anything like me, you hate long prefaces. I don’t care about the author’s background. Nor do I need a lengthy overview of the history of what I am about to learn. Just tell me what I am going to learn, and then teach me how to do it. You are about to learn, through a series of projects, how to use a set of modern neural network tools to forecast time series data using R. Here are the tools: • Deep Neural Networks. • Long Short-Term Memory Neural Network. • Gated Recurrent Unit Neural Network. • Simple Recurrent Neural Network. • Elman Neural Network. • Jordan Neural Network. • General Method of Data Handling Type Neural Net- works. And here are the projects: • Predicting the Number of New Cases of Escherichia coli. • Forecasting the Whole Bird Spot Price of Chicken. • Predicting Eye Movements. • Forecasting Electrocardiogram Activity. • Historical Wheat Price Forecasting. • Modeling Air Temperature. • Forecasting Monthly Sunspot Numbers.

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