第四章時(shí)間序列分析法ppt課件

1、市場(chǎng)預(yù)測(cè)與管理決策Forecasting Market and Decision Making馮燕1862 7016 225: 6326 48182本章構(gòu)造 時(shí)間序列分析法的特點(diǎn)與步驟1.簡(jiǎn)易平均法2.挪動(dòng)平均法3.指數(shù)平滑法4.趨勢(shì)延伸法5.4.1 定義、特點(diǎn)與步驟什么是時(shí)間序列分析法？時(shí)間序列分析法的特點(diǎn)：根據(jù)過(guò)去變化趨勢(shì)，預(yù)測(cè)未來(lái)開(kāi)展時(shí)間序列數(shù)據(jù)變動(dòng)存在著規(guī)律性和不規(guī)律性撇開(kāi)市場(chǎng)開(kāi)展的因果關(guān)系時(shí)間序列市場(chǎng)預(yù)測(cè)法的步驟什么是時(shí)間序列分析法？時(shí)間序列：市場(chǎng)景象的統(tǒng)計(jì)目的數(shù)值，按時(shí)間先后順序陳列而成的數(shù)列。時(shí)間序列分析法：分析和研討，建立預(yù)測(cè)模型，預(yù)測(cè)編制時(shí)間序列要

2、做到：總體范圍一致；代表的時(shí)間單位長(zhǎng)短一致；統(tǒng)計(jì)數(shù)值的計(jì)算方法和計(jì)量單位一致。 Time Series Analysisuse past data to forecast futurepast data should be availableuseful for short termuseful for stable datauseful for forecasting for large number of itemscan not predict turning points (lag effect)時(shí)間序列分析法的特點(diǎn)一、前提是假定事物的過(guò)去會(huì)延續(xù)到未來(lái)。 未來(lái)開(kāi)展過(guò)去歷史的簡(jiǎn)單反復(fù) 短

3、期市場(chǎng)預(yù)測(cè) 中長(zhǎng)期市場(chǎng)預(yù)測(cè) 時(shí)間序列分析法的特點(diǎn)二、時(shí)間序列數(shù)據(jù)變動(dòng)存在著規(guī)律性與不規(guī)律性 長(zhǎng)期趨勢(shì)變動(dòng)T 季節(jié)變動(dòng)S 循環(huán)變動(dòng)C 不規(guī)那么變動(dòng)I 乘法模型：時(shí)間序列分析法的特點(diǎn)三、撇開(kāi)因果關(guān)系 將一切的影響要素歸結(jié)到時(shí)間這一要素上，只成認(rèn)一切影響要素的綜協(xié)作用 時(shí)間序列分析法的步驟搜集、整理時(shí)間序列，繪制圖形對(duì)時(shí)間序列進(jìn)展分析選擇預(yù)測(cè)方法，建立預(yù)測(cè)模型測(cè)算預(yù)測(cè)誤差誤差度量絕對(duì)相對(duì)平均平均絕對(duì)均方差百分平均百分平均絕對(duì)百分4.2 簡(jiǎn)易平均法簡(jiǎn)單算術(shù)平均法加權(quán)平均法幾何平均法Example:Forecasting at FastchipsFastchips is a leading produc

4、er of microprocessors.Six months ago, it launched the sales of its latest microprocessor.Month-by-month sales (in thousands) over the initial six months have been172524263028Question: What is the forecast for next months sales?The Last-Value Forecasting MethodThe last-value forecasting method ignore

5、s all data points in a time series except the last one.Forecast = Last valueFastchips: Month-by-month sales (in thousands) over the initial six months:172524263028Forecast = 28Reasonable when conditions tend to change so quickly that sales before the last months are not a reliable of future sales. 簡(jiǎn)

6、單算術(shù)平均法將察看期內(nèi)預(yù)測(cè)目的時(shí)間序列值加總平均，求得算術(shù)平均數(shù)，作為下期預(yù)測(cè)值。時(shí)間序列數(shù)據(jù)方差越小，簡(jiǎn)單平均數(shù)作為預(yù)測(cè)值的代表性越好。缺陷：一切察看值不論新舊在預(yù)測(cè)中一概同等對(duì)待，這不符合市場(chǎng)開(kāi)展的實(shí)踐情況。 The Averaging Forecasting MethodThe averaging forecasting method uses all the data points in the time series and simply averages these points.Forecast = Average of all data to dateFastchips: Mo

7、nth-by-month sales (in thousands) over the initial six months:172524263028Forecast = (17+25+24+26+30+28) / 6 = 25Reasonable when conditions tend to remain so stable that even the earliest sales reliable indicator.加權(quán)平均法根據(jù)察看值重要性不同對(duì)影響大的近期察看值給予較大的權(quán)數(shù)，對(duì)影響小的遠(yuǎn)期察看值那么給予較小的權(quán)數(shù)，分別給予相應(yīng)的權(quán)數(shù)，再計(jì)算加權(quán)平均數(shù)作為建立預(yù)測(cè)模型的方法。權(quán)數(shù)確實(shí)

8、定：距預(yù)測(cè)期的遠(yuǎn)近，動(dòng)搖幅度大小缺陷：對(duì)于趨勢(shì)變動(dòng)明顯的時(shí)間序列，無(wú)論怎樣的加大權(quán)數(shù)， 也跟不上實(shí)踐值的變動(dòng)，它小于后期的實(shí)踐察看值，更不能作為預(yù)測(cè)值。 幾何平均法 計(jì)算出一定時(shí)期內(nèi)預(yù)測(cè)目的時(shí)間序列的開(kāi)展速度或逐期增長(zhǎng)率，然后在此根底上進(jìn)展預(yù)測(cè)。適用于趨勢(shì)變動(dòng)規(guī)律表現(xiàn)為開(kāi)展速度一樣的時(shí)間序列。4.3 挪動(dòng)平均法挪動(dòng)平均法的概念和特點(diǎn)一次挪動(dòng)平均法加權(quán)挪動(dòng)平均法挪動(dòng)平均法的概念和特點(diǎn)對(duì)時(shí)間序列察看值由遠(yuǎn)到近按一定跨越期計(jì)算平均值的預(yù)測(cè)方法。適宜于既有趨勢(shì)變動(dòng)，又有動(dòng)搖的時(shí)間序列。一次挪動(dòng)平均法由延續(xù)挪動(dòng)構(gòu)成的各組數(shù)據(jù)，用算術(shù)平均法計(jì)算各組數(shù)據(jù)的挪動(dòng)平均值。缺陷：只能向未來(lái)預(yù)測(cè)一期對(duì)于有明顯趨勢(shì)變

9、動(dòng)的市場(chǎng)景象時(shí)間序列不適宜。由于一次挪動(dòng)平均值大大滯后于實(shí)踐察看值。The Moving-Average Forecasting MethodThe moving-average forecasting method averages the data for only the most recent time periods.n = Number of recent periods to consider as relevant for forecastingForecast = Average of last n valuesFastchips: Month-by-month sales

10、(in thousands) over the initial six months:172524263028Forecast (n=3) = (26+30+28) / 3 = 28 17252426302828Forecast (n=3) = (30+28+28) / 3 = 29Reasonable when conditions tend to change occasionally but not extremely rapidly.加權(quán)挪動(dòng)平均法對(duì)市場(chǎng)景象察看值按距預(yù)測(cè)期的遠(yuǎn)近給予不同的權(quán)數(shù)，并按其加權(quán)計(jì)算出挪動(dòng)平均值。The Weighted Moving-Average Fore

11、casting MethodThe weighted moving-average forecasting method averages the data for only the most recent time periods with weighted.n = Number of recent periods to consider as relevant for forecastingForecast = Weighted Average of last n valuesFastchips: Month-by-month sales (in thousands) over the i

12、nitial six months:172524263028Forecast (n=3, w1=0.2, w2=0.3, w3=0.5) = (26*0.2+30*0.3+28*0.5) = 28.217252426302828.2Forecast (n=3 , w1=0.2, w2=0.3, w3=0.5) = (30*0.2+28*0.3+28.2*0.5) = 28.54.4 指數(shù)平滑法指數(shù)平滑法的概念和特點(diǎn)一次指數(shù)平滑法指數(shù)平滑法的概念和特點(diǎn)特殊的加權(quán)挪動(dòng)平均法。特點(diǎn)：離預(yù)測(cè)期最近的察看值給予最大的權(quán)數(shù)察看值對(duì)預(yù)測(cè)值的影響由遠(yuǎn)及近按等比數(shù)列減小，其首項(xiàng)是 ，公比為 。預(yù)測(cè)值可以經(jīng)過(guò)調(diào)整

13、 的大小來(lái)調(diào)理近期察看值和遠(yuǎn)期察看值對(duì)預(yù)測(cè)值的不同影響程度。 The Exponential Smoothing Forecasting MethodThe exponential smoothing forecasting method places the greatest weight on the last value in the time series and then progressively smaller weights on the older values.Forecast = a (Last value) + (1 a) (Last forecast)a is the

14、 smoothing constant between 0 and 1.The choice of the value of the smoothing constant a has a substantial effect on the forecast.A small value (say, 0.1) if conditions are relatively stable.A larger value (say, 0.5) if significant changes occur frequently.ESFt = Ft-1 + (Dt-1 - Ft-1)Example: = 0.2, L

15、et F1 = 1000F2 = 1000 + 0 = 1000F3 = 1000 + 40 = 1040F4 = 1040 + 172 = 1212F5 = 1212 - 82.4 = 1129.6Periods12345678Actual Demand10001200190080011201350?FtF1F2F3F4F5F6F7F8ESFt = Ft-1 + (Dt-1 - Ft-1)Example: = 0.2, Let F1 = 1000F6 = 1129.6 - 1.92 = 1127.68F7 = 1127.68 -44.46 = 1172.14Similar to weight

16、ed moving average exponentially decreasing weights for all previousperiodsPeriods12345678Actual Demand10001200190080011201350?FtF1F2F3F4F5F6F7F8Forecast ErrorMean Absolute Error (MAE)Root Mean Square Error(RMSE)二次挪動(dòng)平均法二次挪動(dòng)平均法是對(duì)時(shí)間序列一次挪動(dòng)平均值再進(jìn)展第二次挪動(dòng)平均，利用第一次挪動(dòng)平均值和二次挪動(dòng)平均值構(gòu)成時(shí)間序列的最后一個(gè)數(shù)據(jù)為根據(jù)建立線性模型進(jìn)展預(yù)測(cè)。二次挪動(dòng)平均

17、法二次挪動(dòng)平均法的特點(diǎn)：二次挪動(dòng)平均法與一次挪動(dòng)平均法相比，其優(yōu)點(diǎn)是大大減少了滯后偏向，使預(yù)測(cè)準(zhǔn)確性提高。二次挪動(dòng)平均只適用于短期預(yù)測(cè)，并且時(shí)間序列數(shù)據(jù)呈現(xiàn)線性趨勢(shì)變化的預(yù)測(cè)。二次挪動(dòng)平均法比一次挪動(dòng)平均法適用面更廣，在實(shí)際中運(yùn)用較多。二次挪動(dòng)平均法例7-4 由于歷史數(shù)據(jù)根本呈線性趨勢(shì)，且又有動(dòng)搖，為靈敏反映其變動(dòng)趨勢(shì)，挪動(dòng)平均的跨越期宜短一些，設(shè)n=3兩次挪動(dòng)的n應(yīng)取值一致 1.計(jì)算一次和二次挪動(dòng)平均值一次挪動(dòng)平均值：二次挪動(dòng)平均值：2.計(jì)算各期a，b 值3.計(jì)算察看期內(nèi)估計(jì)值4.運(yùn)用預(yù)測(cè)模型計(jì)算預(yù)測(cè)值應(yīng)該留意的是，察看期內(nèi)各期估計(jì)值的a，b值不同，而在預(yù)測(cè)期各預(yù)測(cè)值的a，b值是一致的，即最后一個(gè)察看期的a，b值。上例中a