摘要:對時間序列模型進行優化首先將時序數據分解為趨勢分量,季節周期分量和隨機分量對趨勢分量使用模型進行擬合季節周期分量則使用歷史同期分量隨機分量則是使用歷史同類的平均值進行預測使用面向對象的方式,構造模型的類,自動選取最優的模型參數定義的類計算最
**對時間序列模型進行優化
1.首先將時序數據分解為趨勢分量,季節周期分量和隨機分量
2.對趨勢分量使用ARIMA模型進行擬合
3.季節周期分量則使用歷史同期分量
4.隨機分量則是使用歷史同類的平均值進行預測
5.使用面向對象的方式,構造模型的類,自動選取最優的模型參數**
import numpy as np import pandas as pd from datetime import datetime import matplotlib.pylab as plt from statsmodels.tsa.stattools import adfuller import pandas as pd import matplotlib.pyplot as plt import numpy as np from statsmodels.graphics.tsaplots import plot_acf,plot_pacf import sys from dateutil.relativedelta import relativedelta from copy import deepcopy from statsmodels.tsa.arima_model import ARMA import warnings warnings.filterwarnings("ignore")```定義ARIMA的類
class arima_model:
def __init__(self,ts,maxLag = 9): self.data_ts = ts self.resid_ts = None self.predict_ts = None self.forecast_ts = None self.maxLag = maxLag self.p = maxLag self.q = maxLag self.properModel = None self.bic = sys.maxsize #計算最優的ARIMA模型,將相關結果賦給相應的屬性 def get_proper_model(self): self._proper_model() self.predict_ts = deepcopy(self.properModel.predict()) self.resid_ts = deepcopy(self.properModel.resid) self.forecast_ts = deepcopy(self.properModel.forecast()) #對于給定范圍內的p,q計算擬合得最好的arima模型,這里是對差分好的數據進行擬合,故差分恒為0 def _proper_model(self): for p in np.arange(self.maxLag): for q in np.arange(self.maxLag): model = ARMA(self.data_ts, order = (p,q)) try: results_ARMA = model.fit(disp = -1, method = "css") except: continue bic = results_ARMA.bic if bic < self.bic: self.p = p self.q = q self.properModel = results_ARMA self.bic = bic self.resid_ts = deepcopy(self.properModel.resid) self.predict_ts = self.properModel.predict() #參數確定模型 def certain_model(self,p,q): model = ARMA(self.data_ts,order = (p,q)) try: self.properModel = model.fit(disp = -1,method = "css") self.p = p self.q = q self.bic = self.properModel.bic self.predict_ts = self.properModel.predict() self.resid_ts = deepcopy(self.properModel.resid) self.forecast_ts = self.properModel.forecast() except: print ("You can not fit the model with this parameter p,q")```
dateparse = lambda dates:pd.datetime.strptime(dates,"%Y-%m") #paese_dates指定日期在哪列 index_dates將年月日的哪個作為索引,date_parser將字符串轉為日期 f = open("D:福建AirPassengers.csv") data = pd.read_csv(f, parse_dates=["Month"],index_col="Month",date_parser=dateparse) ts = data["#Passengers"]
def draw_ts(timeSeries,title): f = plt.figure(facecolor = "white") timeSeries.plot(color = "blue") plt.title(title) plt.show() def seasonal_decompose(ts): from statsmodels.tsa.seasonal import seasonal_decompose decomposition = seasonal_decompose(ts, model = "multiplicative") trend = decomposition.trend seasonal = decomposition.seasonal residual = decomposition.resid draw_ts(ts,"origin") draw_ts(trend,"trend") draw_ts(seasonal,"seasonal") draw_ts(residual,"residual") return trend,seasonal,residual def testStationarity(ts): dftest = adfuller(ts) # 對上述函數求得的值進行語義描述 dfoutput = pd.Series(dftest[0:4], index=["Test Statistic","p-value","#Lags Used","Number of Observations Used"]) for key,value in dftest[4].items(): dfoutput["Critical Value (%s)"%key] = value # print ("dfoutput",dfoutput) return dfoutput
ts_log = np.log(ts) trend,seasonal,residual = seasonal_decompose(ts_log) seasonal_arr = seasonal residual = residual.dropna() residual_mean = np.mean(residual.values) trend = trend.dropna()
代碼運行如下:
#將原始數據分解為趨勢分量,季節周期和隨機分量 #對trend進行平穩定檢驗 testStationarity(trend)
#對序列進行平穩定處理 trend_diff_1 = trend.diff(1) trend_diff_1 = trend_diff_1.dropna() draw_ts(trend_diff_1,"trend_diff_1") testStationarity(trend_diff_1) trend_diff_2 = trend_diff_1.diff(1) trend_diff_2 = trend_diff_2.dropna() draw_ts(trend_diff_2,"trend_diff_2") testStationarity(trend_diff_2)
#使用模型擬合趨勢分量 #使用模型參數的自動識別 model = arima_model(trend_diff_2) model.get_proper_model() predict_ts = model.properModel.predict() #還原數據,因為使用的是乘法模型,將趨勢分量還原之后需要乘以對應的季節周期分量和隨機分量 diff_shift_ts = trend_diff_1.shift(1) diff_recover_1 = predict_ts.add(diff_shift_ts) rol_shift_ts = trend.shift(1) diff_recover = diff_recover_1.add(rol_shift_ts) recover = diff_recover["1950-1":"1960-6"] * seasonal_arr["1950-1":"1960-6"] * residual_mean log_recover = np.exp(recover) draw_ts(log_recover,"log_recover")
#模型評價 ts_quantum = ts["1950-1":"1960-6"] plt.figure(facecolor = "white") log_recover.plot(color = "blue",label = "Predict") ts_quantum.plot(color = "red", label = "Original") plt.legend(loc = "best") plt.title("RMSE %.4f" % np.sqrt(sum((ts_quantum - log_recover) ** 2) / ts_quantum.size)) plt.show()
文章版權歸作者所有,未經允許請勿轉載,若此文章存在違規行為,您可以聯系管理員刪除。
轉載請注明本文地址:http://specialneedsforspecialkids.com/yun/41868.html
摘要:預測事件本質上是我們通過機器學習預測系統,創造出來的一個假想事件,并根據預測閾值的不同,可以在下載安裝及最終付費之間做優化調節。目前,此機器學習系統已在行業內上線,每天會分析預測上百萬用戶,幫助他們優化游戲內及廣告體驗。 近年來,移動端游戲隨著智能手機技術的發展,越來越成為人們娛樂休閑的新模式。據 NewZoo 數據調查研究發現,全球手機端游戲已達到 21 億玩家規模,呈 14% 同比年增長...
摘要:經過一段時間的說句搜集,當具備一定的數據量時,你就可以用通過機器學習算法來執行一些有用的分析并產生一些有價值的推薦了。 翻譯自?Google Cloud Platform 原文標題:Using Machine Learning on Compute Engine to Make Product Recommendations 原文地址:https://cloud.google.com/...
閱讀 2953·2021-11-23 09:51
閱讀 1006·2021-09-26 09:55
閱讀 3935·2021-09-22 14:58
閱讀 1468·2021-09-08 09:35
閱讀 1078·2021-08-26 14:16
閱讀 881·2019-08-23 18:17
閱讀 2053·2019-08-23 16:45
閱讀 700·2019-08-23 15:55