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Practical Time Series Analysis

Курс от The State University of New York
Средний≈ 26 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future. Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn. You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself! Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!

Навыки, которые вы освоите

Time Series Analysis and ForecastingR ProgrammingStatistical ModelingMathematical ModelingForecastingModel EvaluationDescriptive StatisticsCorrelation AnalysisPredictive AnalyticsStatistical ProgrammingPredictive ModelingR (Software)Statistical VisualizationStatistical SoftwareData VisualizationData AnalysisStatistical MethodsStatistical InferenceData TransformationStatistical Analysis

Программа курса

6 модулей · 118 учебных материалов

01WEEK 1: Basic Statistics21 материалов

Welcome to Practical Time Series Analysis

Course IntroductionВидео

R software setup

Week 1 Welcome VideoВидеоWelcome to Week 1ЧтениеGetting Started with RЧтение

Учитесь у экспертов

Tural Sadigov

Lecturer

William Thistleton

Associate Professor

Practical Time Series Analysis
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Обучение на Coursera

≈ 26 ч

6 модулей

Язык: Английский

Субтитры: Арабский, Французский, Бенгальский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Урду, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Getting Started in R: Download and Install R on WindowsВидео
Getting Started in R: Download and Install R on MacВидео
Getting Started in R: Using PackagesВидео

Review of basic descriptive and inferential statistics

Codes for Concatenation, Five-number summary, Standard DeviationЛабораторнаяConcatenation, Five-number summary, Standard DeviationВидеоCodes for HistogramЛабораторнаяHistogram in RВидеоCodes for ScatterplotЛабораторнаяScatterplot in RВидеоBasic Statistics Review (with linear regression and hypothesis testing)ЧтениеReview of Basic Statistics I - Simple Linear RegressionВидеоReviewing Basic Statistics II More Linear RegressionВидеоReviewing Basic Statistics III - InferenceВидеоMeasuring Linear Association with the Correlation FunctionЧтениеReviewing Basic Statistics IVВидеоVisualizationЗаданиеBasic Statistics ReviewЗадание
02Week 2: Visualizing Time Series, and Beginning to Model Time Series15 материалов

Welcome to Week 2

Week 2 Welcome VideoВидео

Visualizing and Describing a Time Series: time plots, autocovariance, and autocorrelation

All slides together for the next two lessonsЧтениеIntroductionВидеоTime plotsВидеоFirst Intuitions on (Weak) StationarityВидеоAutocovariance functionВидеоAutocovariance coefficientsВидеоAutocorrelation Function (ACF)ВидеоNoise Versus SignalЗадание

How do we model Time Series? Random Walks and an Introduction to Moving Averages

Random WalkВидеоIntroduction to Moving Average ProcessesВидеоSimulating MA(2) process - codes for the next video lectureЛабораторнаяSimulating MA(2) processВидеоRandom Walk vs Purely Random ProcessЗаданиеTime plots, Stationarity, ACV, ACF, Random Walk and MA processesЗадание
03Week 3: Stationarity, MA(q) and AR(p) processes24 материалов

Welcome to Week 3

Week 3 Welcome VideoВидео

Stationarity: generalizing from an individual to a group

Stationarity - Examples -White Noise, Random Walks, and Moving AveragesЧтениеStationarity - Intuition and DefinitionЧтениеStationarity - ACF of a Moving AverageЧтениеStationarity - Intuition and DefinitionВидеоStationarity - First Examples...White Noise and Random WalksВидеоStationarity - First Examples...ACF of Moving AverageВидеоStationarityЗадание

The Backshift Operator applied to MA(q) and AR(p) processes.

All slides together for lesson 2 and 4ЧтениеSeries and Series RepresentationВидеоBackward shift operatorВидеоIntroduction to InvertibilityВидеоDualityВидеоMean Square Convergence (Optional)Видео

AR(p) processes

Autoregressive Processes- Definition and First ExamplesЧтениеAutoregressive Processes - Definition, Simulation, and First ExamplesВидеоAutoregressive Processes - Backshift Operator and the ACFЧтениеAutoregressive Processes - Backshift Operator and the ACFВидеоAR(p) and the ACFЗадание

Intro to Yule- Walker equations.

Difference equationsВидеоYule - Walker equationsВидеоYule - Walker equations - SlidesЧтениеDifference equations and Yule-Walker equationsЗадание
04Week 4: AR(p) processes, Yule-Walker equations, PACF18 материалов

Welcome to Week 4

Week 4 Welcome VideoВидео

Employ PACF to estimate the order of AR( p ) processes.

Partial Autocorrelation and the PACF First ExamplesЧтениеPartial Autocorrelation and the PACF First ExamplesВидеоPartial Autocorrelation and the PACF: Concept DevelopmentЧтениеPartial Autocorrelation and the PACF - Concept DevelopmentВидеоPartial AutocorrelationЗадание

Write Yule-Walker equations in matrix notation, and estimate model parameters in AR(p) processes.

All slides together for the next two lessonsЧтениеYule-Walker Equations in Matrix FormВидеоAR(2) Simulation (Parameter Estimation)ЛабораторнаяYule Walker Estimation - AR(2) SimulationВидеоAR(3) Simulation (Parameter Estimation)ЛабораторнаяYule Walker Estimation - AR(3) SimulationВидео

AR processes - Data Oriented Examples

Recruitment - model fittingЛабораторнаяRecruitment data - model fittingВидеоJohnson & Johnson-model fittingЛабораторнаяJohnson & Johnson-model fittingВидео'LakeHuron' datasetЗадание
05Week 5: Akaike Information Criterion (AIC), Mixed Models, Integrated Models19 материалов

Welcome to Week 5

Week 5 Welcome VideoВидео

Judge the quality of the fitted model by using Akaike Information Criterion (AIC).

Akaike Information Criterion and Model QualityЧтениеAkaike Information Criterion and Model QualityВидеоAIC and model buildingЗадание

ARMA

ARMA Models and a Little TheoryЧтениеARMA Models (And a Little Theory)ВидеоARMA Properties and ExamplesЧтениеARMA Properties and ExamplesВидеоARMA ProcessesЗадание

ARIMA

All slides together for this lessonЧтениеARIMA ProcessesВидеоARIMA(2,1,1) SimulationЛабораторнаяQ-StatisticВидеоARIMA and Q-statisticЗаданиеDaily births in California in 1959ВидеоDaily birth dataset
06Week 6: Seasonality, SARIMA, Forecasting21 материалов

Welcome to Week 6

Week 6 Welcome VideoВидео

SARIMA

All slides together for the next two lessonsЧтениеSARIMA processesВидеоACF of SARIMA modelsВидеоSARIMA simulation (code block)ЧтениеSARIMA processesЗадание

Applications

SARIMA fitting: Johnson & JohnsonВидеоSARIMA code for J&J (code block)ЧтениеSARIMA fitting: Milk productionВидеоSARIMA code for Milk productionЛабораторнаяSARIMA fitting: Sales at a souvenir shopВидеоSARIMA code for Sales at a souvenir shopЛабораторная'USAccDeaths' datasetЗадание

Forecasting with Smoothing Techniques

Forecasting using Simple Exponential SmoothingЧтениеForecasting Using Simple Exponential SmoothingВидеоForecasting Using Holt Winters for Trend (Double Exponential)ЧтениеDouble Exponential SmoothingВидеоForecasting Using Holt Winters for Trend and Seasonality (Triple Exponential)ЧтениеTriple Exponential Smoothing Concept DevelopmentВидео
Series, Backward Shift Operator, Invertibility and DualityЗадание
Yule-Walker in matrix form and Yule-Walker estimationЗадание
Чтение
Daily birth - R codeЛабораторная
Daily female birth (R file)Чтение
'BJsales' datasetЗадание
Triple Exponential Smoothing ImplementationВидео
ForecastingЗадание