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@@ -1525,21 +1525,24 @@ func GetChartPredictEdbInfoDataListByRuleNAnnualAverage(edbInfoId int, configVal
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// AnnualValueInversionConf 年度值倒推规则
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type AnnualValueInversionConf struct {
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- Value float64 `description:"年度值"`
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- Type int `description:"分配方式，1：均值法；2：同比法"`
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- Year int `description:"同比年份"`
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+ Value float64 `description:"年度值"`
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+ Type int `description:"分配方式，1：均值法；2：同比法"`
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+ Year int `description:"同比年份"`
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+ YearList []int `description:"指定年份列表"`
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}
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// GetChartPredictEdbInfoDataListByRuleAnnualValueInversion 根据 年度值倒推 规则获取预测数据
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-// ETA预测规则：年度值倒推：设定年度值，余额=年度值-年初至今累计值(算法参考累计值)，进行余额分配，均值法分配时保证每期数值相等（日度/周度：剩余期数=剩余自然日历天数/今年指标最新日期自然日历天数*今年至今指标数据期数；旬度/月度/季度/半年度：剩余期数=全年期数(36\12\4\2)-今年至今自然日历期数），同比法保证每期同比相等（同比增速=余额/同比年份相应日期的余额，预测值等于同比年份同期值*同比增速）
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-// 举例：
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-// 指标A 日度 最新日期 2023-05-19 年初至今累计值100
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-// 设置年度值1000
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-// 则余额=1000-100=900
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-// 均值法分配：剩余期数=226/139*120=195.11
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-// 今年之后的每一期预测值=900/195.11=4.6128
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-// 同比法分配：同比增速=900/同比年份5.19的余额
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-// 预测值=同比年份5-20的值*（1+同比增速）
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+// 预测指标-年度值倒推
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+// 1、年度值倒推，选择同比法，支持选择多个年份（当前只可选择一个年份）。选择多个年份时，计算多个年份的余额平均，和同期平均。
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+// 2、年度值倒推，同比法的算法优化：旬度，月度，季度，半年度的算法，同原先算法。
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+// 日度、周度值算法更新（假设指标实际值最新日期月2024/3/1）：
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+// 1、设定年度值
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+// 2、计算余额：年度值-年初至今累计值
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+// 3、年初至今累计值计算方法：用后置填充变频成连续自然日日度数据。计算1/1至指标最新日期(2024/3/3/1)的累计值。
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+// 4、计算同比年份全年累计值，年初至指标最新值同期(2023/3/1)累计值，两者相减得到同比年份同期余额，再取平均值，作为最终的同期余额
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+// 5、用今年余额/去年同期余额得到同比增速。
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+// 6、每一期预测值，为同比年份的同期值，乘以（1+同比）。去年同期，用变频后的序列对应。
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+// 7、如果选择的同比年份是多个。则计算多个年份的平均余额。今年余额/平均余额=同比增速。同比基数为多个年份的同期平均值
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func GetChartPredictEdbInfoDataListByRuleAnnualValueInversion(edbInfoId int, configValue string, dayList []time.Time, frequency string, realPredictEdbInfoData, predictEdbInfoData []*data_manage.EdbDataList, existMap map[string]float64) (newPredictEdbInfoData []*data_manage.EdbDataList, minValue, maxValue float64, err error) {
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if frequency == "年度" {
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err = errors.New("当前指标频度是年度，不允许配置年度值倒推")
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@@ -1560,6 +1563,11 @@ func GetChartPredictEdbInfoDataListByRuleAnnualValueInversion(edbInfoId int, con
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newPredictEdbInfoData = predictEdbInfoData
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index := len(allDataList)
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+ // 没有数据，直接返回
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+ if index <= 0 {
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+ return
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+ }
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+
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// 配置的年度值
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yearValueConfig := annualValueInversionConf.Value
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@@ -1708,112 +1716,166 @@ func GetChartPredictEdbInfoDataListByRuleAnnualValueInversion(edbInfoId int, con
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// 同比法分配
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// 同比法保证每期同比相等（同比增速=余额/同比年份相应日期的余额，预测值等于同比年份同期值*同比增速）；
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// 同比法分配：同比增速=900/同比年份5.19的余额
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-
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+ yearList := annualValueInversionConf.YearList
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+ if len(yearList) == 0 {
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+ //兼容历史数据
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+ yearList = append(yearList, annualValueInversionConf.Year)
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+ }
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// 每年截止到当前日期的累计值
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dateTotalMap := make(map[time.Time]float64)
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+
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+ //把每一期的期数和日期绑定
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+ dateIndexMap := make(map[time.Time]int)
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+ indexDateMap := make(map[int]time.Time)
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// 每年的累计值（计算使用）
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yearTotalMap := make(map[int]float64)
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- for _, v := range allDataList {
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- currTime, tmpErr := time.ParseInLocation(utils.FormatDate, v.DataTime, time.Local)
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- if tmpErr != nil {
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- err = tmpErr
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- return
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+ //数据按找后值填充的方式处理成连续自然日日度数据
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+ allDataListMap := make(map[string]float64)
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+ // todo 如果是日度和周度，用后置填充变频成连续自然日日度数据。计算1/1至指标最新日期(2024/3/3/1)的累计值
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+ switch frequency {
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+ case "日度", "周度":
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+ for _, v := range allDataList {
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+ allDataListMap[v.DataTime] = v.Value
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+ }
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+ //找到最早日期的的年份的1月1日，转成time格式
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+ earliestYear := allDataList[0].DataTime[:4]
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+ earliestYearFirstDay, _ := time.ParseInLocation(utils.FormatDate, earliestYear+"-01-01", time.Local)
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+ days := int(currDayTime.Sub(earliestYearFirstDay).Hours() / float64(24))
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+ //循环累加日期，直到循环到最新日期
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+ for i := 0; i <= days; i++ {
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+ currentDate := earliestYearFirstDay.AddDate(0, 0, i)
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+ currentDateStr := currentDate.Format(utils.FormatDate)
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+ val, ok := allDataListMap[currentDateStr]
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+ if !ok { //如果不存在，则填充后值
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+ //循环向后查找数据，直到找到
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+ for j := i + 1; j <= days; j++ {
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+ //循环往后取值
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+ currentDateTmp := earliestYearFirstDay.AddDate(0, 0, j)
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+ currentDateTmpStr := currentDateTmp.Format(utils.FormatDate)
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+ if tmpVal, ok1 := allDataListMap[currentDateTmpStr]; ok1 {
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+ allDataListMap[currentDateStr] = tmpVal
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+ val = tmpVal
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+ break
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+ }
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+ }
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+ }
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+ //计算每一天的年初至今累计值
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+ yearVal := yearTotalMap[currentDate.Year()]
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+ if frequency == "周度" {
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+ // 每日累计值需要当前值除7
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+ yearVal = yearVal + val/7
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+ } else {
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+ yearVal = yearVal + val
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+ }
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+ yearTotalMap[currentDate.Year()] = yearVal
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+ dateTotalMap[currentDate] = yearVal
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+ dateIndexMap[currentDate] = i
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+ indexDateMap[i] = currentDate
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}
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- yearVal := yearTotalMap[currTime.Year()]
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- yearVal = yearVal + v.Value
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- yearTotalMap[currTime.Year()] = yearVal
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- dateTotalMap[currTime] = yearVal
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- }
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-
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- //（同比增速=余额/同比年份相应日期的余额，预测值等于同比年份同期值*同比增速）；
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- for k, currentDate := range dayList {
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- currYearBalance := yearValueConfig - yearTotalMap[currentDate.Year()] // 当年的余额
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-
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- // 上一期的日期
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- prevDateStr := allDataList[len(allDataList)-1].DataTime
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- prevDateTime, tmpErr := time.ParseInLocation(utils.FormatDate, prevDateStr, time.Local)
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- if tmpErr != nil {
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- err = tmpErr
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- return
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+ default:
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+ for k, v := range allDataList {
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+ currTime, tmpErr := time.ParseInLocation(utils.FormatDate, v.DataTime, time.Local)
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+ if tmpErr != nil {
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+ err = tmpErr
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+ return
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+ }
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+ allDataListMap[v.DataTime] = v.Value
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+ yearVal := yearTotalMap[currTime.Year()]
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+ yearVal = yearVal + v.Value
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+ yearTotalMap[currTime.Year()] = yearVal
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+ dateTotalMap[currTime] = yearVal
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+ dateIndexMap[currTime] = k
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+ indexDateMap[k] = currTime
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}
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-
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- //同比年份相应日期
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- lastYear := annualValueInversionConf.Year + (currentDate.Year() - currDayTime.Year())
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-
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- // 前N年的上一期时间；前N年的当期时间；
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- var lastPrevDateTime, lastDateTime time.Time
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-
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- switch frequency {
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- case "半年度", "季度":
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- lastDateTime = time.Date(lastYear, currentDate.Month(), currentDate.Day(), 0, 0, 0, 0, currentDate.Location())
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- lastPrevDateTime = time.Date(lastYear, prevDateTime.Month(), prevDateTime.Day(), 0, 0, 0, 0, prevDateTime.Location())
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- case "月度":
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- lastDateTime = time.Date(lastYear, currentDate.Month()+1, 1, 0, 0, 0, 0, currentDate.Location()).AddDate(0, 0, -1)
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- lastPrevDateTime = time.Date(lastYear, prevDateTime.Month()+1, 1, 0, 0, 0, 0, prevDateTime.Location()).AddDate(0, 0, -1)
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- case "旬度":
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- if prevDateTime.Day() == 10 || prevDateTime.Day() == 20 {
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- lastDateTime = time.Date(lastYear, currentDate.Month(), currentDate.Day(), 0, 0, 0, 0, currentDate.Location())
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- lastPrevDateTime = time.Date(lastYear, prevDateTime.Month(), prevDateTime.Day(), 0, 0, 0, 0, prevDateTime.Location())
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- } else {
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- lastDateTime = time.Date(lastYear, currentDate.Month()+1, 1, 0, 0, 0, 0, currentDate.Location()).AddDate(0, 0, -1)
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- lastPrevDateTime = time.Date(lastYear, prevDateTime.Month()+1, 1, 0, 0, 0, 0, prevDateTime.Location()).AddDate(0, 0, -1)
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+ }
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+ // 当年的余额
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+ currYearBalance := yearValueConfig - yearTotalMap[currDayTime.Year()]
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+ //fmt.Printf("当年的余额%.4f=给定额度%.4f-当年累计值%.4f\n", currYearBalance, yearValueConfig, yearTotalMap[currDayTime.Year()])
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+ // 循环统计同比年份同期余额
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+ var sum, avg float64
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+ for _, year := range yearList {
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+ yearTotal := yearTotalMap[year]
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+ //fmt.Printf("同比年份的累计值%.4f\n", yearTotal)
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+ tmpDate := time.Date(year, currDayTime.Month(), currDayTime.Day(), 0, 0, 0, 0, currDayTime.Location())
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+ //fmt.Printf("同比年份的同期%s\n", tmpDate)
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+ dateTotal, ok := dateTotalMap[tmpDate]
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+ //fmt.Printf("同比年份的同期累计值%.4f\n", dateTotal)
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+ if ok {
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+ sum = sum + (yearTotal - dateTotal)
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+ } else {
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+ // 查找下一期的余额
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+ tmpIndex, ok1 := dateIndexMap[tmpDate]
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+ if ok1 {
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+ for tmpDateTime := indexDateMap[tmpIndex+1]; tmpDateTime.Year() == year; tmpDateTime = indexDateMap[tmpIndex+1] {
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+ dateTotal, ok = dateTotalMap[tmpDateTime]
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+ if ok {
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+ //fmt.Printf("同比年份的同期累计值%.4f\n", dateTotal)
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+ sum = sum + (yearTotal - dateTotal)
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+ break
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+ }
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+ tmpIndex += 1
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+ }
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}
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- case "周度", "日度":
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- lastDateTime = time.Date(lastYear, currentDate.Month(), currentDate.Day(), 0, 0, 0, 0, currentDate.Location())
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- lastPrevDateTime = time.Date(lastYear, prevDateTime.Month(), prevDateTime.Day(), 0, 0, 0, 0, prevDateTime.Location())
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}
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-
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- // 同比年份相应日期的累计值
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- var dateTotal float64
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-
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- dateTotal, ok := dateTotalMap[lastPrevDateTime]
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- if !ok { //如果没有找到这个日期，那么就往前面找，一直到找到这个累计值，或者找完这一年
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- yearFirstDayTime := time.Date(lastPrevDateTime.Year(), 1, 1, 0, 0, 0, 0, lastDateTime.Location())
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- for tmpDateTime := lastPrevDateTime.AddDate(0, 0, -1); tmpDateTime.After(yearFirstDayTime) || tmpDateTime.Equal(yearFirstDayTime); tmpDateTime = tmpDateTime.AddDate(0, 0, -1) {
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- dateTotal, ok = dateTotalMap[tmpDateTime]
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- if ok {
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- break
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+ }
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+ //fmt.Printf("同比年份的余额%.4f\n", sum)
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+ avg = sum / float64(len(yearList))
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+ //fmt.Printf("同比年份的余额%.4f\n", avg)
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+ // 同比增速=当年余额/同比年份上一期日期的余额
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+ tbVal := decimal.NewFromFloat(currYearBalance).Div(decimal.NewFromFloat(avg))
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+ /*tbVal11, _ := tbVal.Round(4).Float64()
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+ fmt.Printf("同比增速%.4f\n", tbVal11)*/
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+ //（同比增速=余额/同比年份相应日期的余额的平均值，预测值等于同比年份同期值*同比增速）；
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+ for k, currentDate := range dayList {
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+ // 循环遍历多个同比年份
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+ var valSum float64
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+ for _, year := range yearList {
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+ //多个同比年份的同期值的平均值
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+ tmpCurrentDate := time.Date(year, currentDate.Month(), currentDate.Day(), 0, 0, 0, 0, currentDate.Location())
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+ if tmpVal, ok := allDataListMap[tmpCurrentDate.Format(utils.FormatDate)]; ok {
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+ valSum += tmpVal
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+ } else {
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+ // 查找下一期的余额
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+ tmpIndex, ok1 := dateIndexMap[tmpCurrentDate]
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+ if ok1 {
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+ for tmpDateTime := indexDateMap[tmpIndex+1]; tmpDateTime.Year() == year; tmpDateTime = indexDateMap[tmpIndex+1] {
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+ tmpVal, ok = allDataListMap[tmpDateTime.Format(utils.FormatDate)]
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+ if ok {
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+ valSum += tmpVal
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+ break
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+ }
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+ tmpIndex += 1
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+ }
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}
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}
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}
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+ lastDateVal := valSum / float64(len(yearList))
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- //同比年份相应的上一期日期的余额
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- lastYearDateBalance := yearTotalMap[lastPrevDateTime.Year()] - dateTotal
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- if lastYearDateBalance == 0 {
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- continue
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+ //预测值 = 同比年份同期值*同比增速
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+ tmpVal, _ := decimal.NewFromFloat(lastDateVal).Mul(tbVal).Round(4).Float64()
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+ currentDateStr := currentDate.Format(utils.FormatDate)
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+ tmpData := &data_manage.EdbDataList{
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+ EdbDataId: edbInfoId + 100000 + index + k,
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+ EdbInfoId: edbInfoId,
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+ DataTime: currentDateStr,
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+ Value: tmpVal,
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+ DataTimestamp: currentDate.UnixNano() / 1e6,
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}
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+ newPredictEdbInfoData = append(newPredictEdbInfoData, tmpData)
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+ allDataList = append(allDataList, tmpData)
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+ existMap[currentDateStr] = tmpVal
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- // 同比增速=当年余额/同比年份上一期日期的余额
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- tbVal := decimal.NewFromFloat(currYearBalance).Div(decimal.NewFromFloat(lastYearDateBalance))
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-
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- // 获取同比年份同期值，获取失败的话，就不处理
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- if lastDateVal, ok := existMap[lastDateTime.Format(utils.FormatDate)]; ok {
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- //预测值 = 同比年份同期值*同比增速
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- tmpVal, _ := decimal.NewFromFloat(lastDateVal).Mul(tbVal).Round(4).Float64()
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- currentDateStr := currentDate.Format(utils.FormatDate)
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- tmpData := &data_manage.EdbDataList{
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- EdbDataId: edbInfoId + 100000 + index + k,
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- EdbInfoId: edbInfoId,
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- DataTime: currentDateStr,
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- Value: tmpVal,
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- DataTimestamp: currentDate.UnixNano() / 1e6,
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- }
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- newPredictEdbInfoData = append(newPredictEdbInfoData, tmpData)
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- allDataList = append(allDataList, tmpData)
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- existMap[currentDateStr] = tmpVal
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-
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- yearVal := yearTotalMap[currentDate.Year()]
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- yearVal = yearVal + tmpVal
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- yearTotalMap[currentDate.Year()] = yearVal
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- dateTotalMap[currentDate] = yearVal
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+ yearVal := yearTotalMap[currentDate.Year()]
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+ yearVal = yearVal + tmpVal
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+ yearTotalMap[currentDate.Year()] = yearVal
|
|
|
+ dateTotalMap[currentDate] = yearVal
|
|
|
|
|
|
- // 最大最小值
|
|
|
- if tmpVal < minValue {
|
|
|
- minValue = tmpVal
|
|
|
- }
|
|
|
- if tmpVal > maxValue {
|
|
|
- maxValue = tmpVal
|
|
|
- }
|
|
|
+ // 最大最小值
|
|
|
+ if tmpVal < minValue {
|
|
|
+ minValue = tmpVal
|
|
|
+ }
|
|
|
+ if tmpVal > maxValue {
|
|
|
+ maxValue = tmpVal
|
|
|
}
|
|
|
}
|
|
|
|
