【latex版】水贴

Title:        Mastering Data Analysis with R        Volume:
Author(s):        Gergely Daroczi
Series:                Periodical:       
Publisher:        Packt Publishing        City:       
Year:        2015        Edition:       
Language:        English        Pages:        380
ISBN:        1783982020, 9781783982028        ID:        1400083
Time added:        2015-10-13 02:32:27        Time modified:        2015-10-13 23:07:51
Library:                Library issue:        0
Size:        8 MB (7952322 bytes)        Extension:        pdf

Forecasting refers to the process of using statistical procedures to predict future values of a time series based on historical trends. For businesses, being able gauge expected outcomes for a given time period is essential for managing marketing, planning, and finances. For example, an advertising agency may want to utilizes sales forecasts to identify which future months may require increased marketing expenditures. Companies may also use forecasts to identify which sales persons met their expected targets for a fiscal quarter.

There are a number of techniques that can be utilized to generate quantitative forecasts. Some methods are fairly simple while others are more robust and incorporate exogenous factors. Regardless of what is utilized, the first step should always be to visualize the data using a line graph. You want to consider how the metric changes over time, whether there is a distinct trend, or if there are distinct patterns that are noteworthy.

1. data <- structure(c(12, 20.5, 21, 15.5, 15.3, 23.5, 24.5, 21.3, 23.5,
   
2.                     28, 24, 15.5, 17.3, 25.3, 25, 36.5, 36.5, 29.6, 30.5, 28, 26,
   
3.                     21.5, 19.7, 19, 16, 20.7, 26.5, 30.6, 32.3, 29.5, 28.3, 31.3,
   
4.                     32.2, 26.4, 23.4, 16.4, 15, 16, 18, 27, 21, 49, 21, 22, 28, 36,
   
5.                     40, 3, 21, 29, 62, 65, 46, 44, 33, 62, 22, 12, 24, 3, 5, 14,
   
6.                     36, 40, 49, 7, 52, 65, 17, 5, 17, 1),
   
7.                   .Dim = c(36L, 2L), .Dimnames = list(NULL, c("Advertising", "Sales")),
   
8.                   .Tsp = c(2006, 2008.91666666667, 12), class = c("mts", "ts", "matrix"))
   
9. head(data); nrow(data)
   
10. plot(data)

复制代码

There are several key concepts that we should be cognizant of when describing time series data. These characteristics will inform how we pre-process the data and select the appropriate modeling technique and parameters. Ultimately, the goal is to simplify the patterns in the historical data by removing known sources of variatiion and making the patterns more consistent across the entire data set. Simpler patterns will generally lead to more accurate forecasts.

Trend: A trend exists when there is a long-term increase or decrease in the data.

Seasonality: A seasonal pattern occurs when a time series is affected by seasonal factors such as the time of the year or the day of the week.

Autocorrelation: Refers to the pheneomena whereby values of Y at time t are impacted by previous values of Y at t-i. To find the proper lag structure and the nature of auto correlated values in your data, use the autocorrelation function plot.

Stationary: A time series is said to be stationary if there is no systematic trend, no systematic change in variance, and if strictly periodic variations or seasonality do not exist

Quantitative forecasting techniques are usually based on reression analysis or time series techniques. Regression approaches examine the relationship between the forecasted variable and other explanatory variables using cross-sectional data. Time series models use hitorical data that’s been collected at regular intervals over time for the target variablle to forecast its future values. There isn’t time to cover the theory behind each of these approaches in this post, so I’ve chosen to cover high level concepts and provide code for performing time series forecasting in R. I strongly suggest understandig the statistical theory behind a technique before running the code.

Quantitative forecasting techniques are usually based on reression analysis or time series techniques. Regression approaches examine the relationship between the forecasted variable and other explanatory variables using cross-sectional data. Time series models use hitorical data that’s been collected at regular intervals over time for the target variablle to forecast its future values. There isn’t time to cover the theory behind each of these approaches in this post, so I’ve chosen to cover high level concepts and provide code for performing time series forecasting in R. I strongly suggest understandig the statistical theory behind a technique before running the code.

First, we can use the ma function in the forecast package to perform forecasting using the moving average method. This technique estimates future values at time t by averaging values of the time series within k periods of t. When the time series is stationary, the moving average can be very effective as the observations are nearby across time.

1982 年，Pories 教授等人在手术治疗病态肥胖症时偶然发现：合并有 2 型糖尿病的患者接受减肥手术后，体重显著减轻的同时血糖也快速恢复了正常, 从此开创了 2 型糖尿病治疗的全新途径——外科手术治疗。

目前已有 RCT 研究证实在 2 年内的短期随访中，手术治疗 2 型糖尿病在控制血糖以及糖尿病并发症方面都比药物治疗显示出更好的效果，但长期的随访中该结果是否会改变，仍缺乏相关研究，为此来自意大利的 Mingrone 教授以及其团队开展了一项随机、非盲、单中心实验性研究，结果于近期发表在 The Lancet 杂志上。

该研究共入组 60 名基本情况相似的 2 型糖尿病病人（入组条件：30-60 岁、BMI ≥ 35、HbA1c ≥ 7%、5 年以上糖尿病史、无严重并发症），分为 3 组，每组 20 人，分别给予药物治疗、Roux-en-Y 胃肠旁路术、胆胰旷置术。随访 5 年后药物组 15 人完成研究，胃肠旁路组和胆胰组均有 19 人完成研究。

对比结果发现，手术病人中，胃肠旁路组 7 人（37%）以及胆胰组 12 人（63%）均在 5 年达到了缓解标准（术后至少一年无其他治疗手段辅助下 FPG ≤ 5.6–6.9 mmol/L，HbA1c<6.5%），而药物组没有任何一人达到该疗效。对比手术和药物治疗效果有显著差异。

将疗效标准放宽至：不服用或服用降糖药后，HbA1c ≤ 6.5%，再进行对比发现药物组里有 4 人（27%）符合标准，而手术病人中胃肠旁路组 8 人（42%）以及胆胰组 13 人（68%）达到了该标准，对比药物与手术效果仍然有显著差异。

值得注意的是，手术病人术后对药物降糖的需求大大减少。33 人（87%）在术后 5 年不需要药物（但需要通过饮食等方式）控制高血糖症。18 位术前需要使用胰岛素降糖的病人，术后 17 位 5 年都无需胰岛素降糖。反观药物组中胰岛素的使用情况却在随访过程中不断增加。

刚刚灌灌灌灌灌灌灌灌灌灌灌灌灌灌灌改革