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Synopsis
The first four chapters mainly introduce the theory, method and prediction practice of time serial. The first chapter presents the general instruction of prediction analysis methods, with the aim to lead the readers know the basic procedure of prediction practice well. The second chapter provides the regression based time prediction practice, mainly including the time regression, autoregression, seasonal based regression, autocorrelation coefficient, ARIMA model and so on. The third chapter explains the smooth based method, mainly containing moving smoothing, seasonal based smoothing, and trend based smoothing methods. The fourth chapter is mainly the introduction and application of ARIMA prediction method. The fifth chapter is the heteroskedasticity model. The sixth chapter is the practice of time series analysis and case study.
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- PublisherTsinghua University Press
- Publication date2012
- ISBN 10 7302291128
- ISBN 13 9787302291121
- BindingPaperback
- LanguageChinese
- Number of pages199
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Time series forecasting practice tutorial (Sino-US economic scholars side by side work)(Chinese Edition)
Published by
Tsinghua University Press, 2012
ISBN 10: 7302291128
ISBN 13: 9787302291121
New
paperback
Seller: liu xing, Nanjing, JS, China
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paperback. Condition: New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Paperback. Pub Date: Unknown in Publisher: Tsinghua University Press List Price: 25.00 yuan Author: Publisher: Tsinghua University Press ISBN: 9787302291121 Yema: Revision: Binding: Folio: Published :2012 -9-1 printing time: Words: Goods ID: 22.871.823 Description time series forecasting the practice tutorial is a focus on the practice of time series analysis and forecasting materials. It to organize the entire contents of the entire process from the time series forecasting. Main content time series prediction process. collection and pre-processing of time-series data. forecast accuracy measure various predictive analysis method. It covers model-based predictive analysis method. also covers data-driven predictive analysis method. The book gives a lot of practical cases. readers can gain the experience necessary for time series prediction. Time series forecasting practice tutorial can be used as a practical time series forecasting analysis textbooks. and can also be used as a reference book of predictive analytics practitioners. Author Introduction Chapter 1 Introduction to the forecasting process the Applications 1.2 book of common symbols forecast 1.3 1.4 1.1 forecast predicted target - the American Railroad Passenger Corporation. for example 1.4.1 target of descriptive and predictive target 1.4.2 forward to predict the number of installments Applications 1.4.4 update 1.4.3 predict and forecast data forecast Chapter 3 Chapter 2 data automation level of 2.1 2.2 time series data collection component of 2.3 time series visualization 2.5 2.4 interactive visualization data preprocessing forecast evaluation of results 3.1 data partitioning 3.1.1 3.1.2 combined training set and validation set time series divided by the time a final prediction model .3.1.3 simple prediction validation set interval 3.2 3.3 3.3.1 commonly used measure of forecast accuracy indicators to measure the prediction accuracy 3.3.2's attention to the problem of measurement model prediction accuracy indicators 3.4 3.4.1 Chapter 4 prediction method of the prediction error prediction interval distribution 3.4.2 Overview 4.1 model-based and data-driven approach 4.2 extrapolation forecast uncertainty assessment. econometric model and external information 4.3 artificial predict and automatically predict 4.4 combination method Chapter 5 a linear trend trend model-based the regression forecasting methods 5.1 Analysis 5.1.1 5.1.2 5.1.3 polynomial trend of the exponential trend 5.2 with seasonal The trend of model 5.3 with both trend and seasonal model the 5.4 forecast 5.5 ar model and arima model 5.5.1 calculated by the model autocorrelation 5.5.2 using self-relevant information to improve forecast accuracy 5.5.3 evaluation predictability 5.6 5.6.2 special events 5.6.1 irregular trend model outliers smoothing method of Chapter 6 6.1 Introduction 6.2 moving average 6.2.1 Center Moving Average: convenient visualization 6.2.2 censored moving average: convenient predicted 6.2.3 choose the window width (w) 6.3 Differential 6.3.1 trend - 6.3.2 6.3.3 Excluding deseasonalised (seasonally adjusted to season) 6.4 trends and seasonal simple exponential smoothing 6.4.1 choose the smoothing parameter 6.4.2 The 6.5 senior index of the relationship between the moving average and simple exponential smoothing smoothing 6.5.1 with the trend of the sequence: 6.5.2 with the trend of the sequence of the additive model: multiplicative model 6.5.3 with trends and seasonal sequence 6.5. multiple seasonal cycles of 4 with the expansion of the seasonal time series (trend) the 6.6 exponential smoothing method 6.6.1 6.6.2 adder 7.1 to predict how the application of external information 7.1.1 Case 1: Chapter 7 smoothing constant prediction method pre tight corner crop yields 7.1.2 Case 2: 7.2 Introduction 7.3.2 Case forecast 7.3 logistic regression 7.3.1 binary outcomes logistic regression predic. Seller Inventory # EJ011820
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