# f = plt.figure(facecolor='white') # timeSeries.plot(color='blue') # plt.show() # def draw_acf_pacf(ts,lags=40): # f = plt.figure(facecolor= 'white') # ax1 = f.add_subplot(211) # plot_acf(ts,lags=40,ax=ax1) # ax2 = f.add_subplot(212) # plot_pacf(ts,lags=40,ax=ax2) # plt.show() test_stationarity.testStationarity(dta) test_stationarity.draw_acf_pacf(dta,l=40) dta_log = np.log(dta) test_stationarity.draw_ts(dta_log) test_stationarity.draw_trend(dta_log,12) # #--------------------------handcraft diff------------------------------------# # diff_12 = dta_log.diff(12) # diff_12.dropna(inplace=True) # diff_12_1 = diff_12.diff(1) # diff_12_1.dropna(inplace=True) # test_stationarity.testStationarity(diff_12_1) # test_stationarity.draw_acf_pacf(diff_12_1) # from statsmodels.tsa.seasonal import seasonal_decompose # decomposition = seasonal_decompose(dta_log,model='multiplicative')
#####################################################################################
"""
for i in range(749):
df = pd.read_csv("../merge_10/"+str(i+1)+".csv",index_col="timeStamp")
# df = pd.read_csv("../merge_10_check/checkin.csv",index_col="timeStamp")
df.index = pd.to_datetime(df.index)
ts = df['passengerCount']
series_list[i]=ts
# print ts.head()
# print ts.head().index
# print df.describe()
# print df.dtypes
# print "check date :",ts['2016-09-10 08:58:02'],ts['2016-09-10'],"http://www.cnblogs.com/foley/p/5582358.html"
"""
#log_tran
"""
# ts_log = np.log(ts)
test_stationarity.draw_ts(series_list)
"""
##########################################################################
# import os
# direction = "../merge_10_add_predict/"
# file_list = os.listdir(direction)
# for file_name in file_list:
# print wifi_name_dict[int(file_name.split(".")[0])]
# file_path = direction+file_name
# df1 = pd.read_csv(file_path,index_col="timeStamp")
# # df = pd.read_csv("../merge_10_check/checkin.csv",index_col="timeStamp")
# df1.index = pd.to_datetime(df1.index)
# df1.sort_index(inplace=True)
# # cols = list(df1)
for row in rows:
# if num == 0:
# last_data = float(row[0])
# if float(row[0]) >400 or float(row[0])<40 :
# data.append(last_data)
# else:
data.append(float(row[0]))
last_data = float(row[0])
time.append(row[1])
num += 1
# x = np.linspace(0, 42, len(data))
# plt.plot(x,data)
# plt.show()
# present = pd.read_csv(file_path,sep = ',')
# print present.shape
# print present.columns
# present_day = present.set_index("data")
# present_day['date'].plot()
# plt.legend(loc = 'best')
# present_day.plot()
# present_day.date.plot(color='g')
# plt.legend(loc = 'best')
# present_day[:10].plot(kind = 'bar')
series_data = pd.Series(data)
draw_trend(series_data, 10)
from test_stationarity import draw_ts
draw_ts(series_data)
from test_stationarity import testStationarity
testStationarity(series_data)
from test_stationarity import draw_acf_pacf
# draw_acf_pacf(series_data)
import numpy as np
import pandas as pd
from datetime import datetime
import matplotlib.pylab as plt
import test_stationarity
from statsmodels.tsa.seasonal import seasonal_decompose
# 读取数据，pd.read_csv默认生成DataFrame对象，需将其转换成Series对象
df = pd.read_csv('AirPassengers.csv', encoding='utf-8', index_col='date')
df.index = pd.to_datetime(df.index) # 将字符串索引转换成时间索引
ts = df['Passengers'] # 生成pd.Series对象
# 查看数据格式
# print ts.head()
# print ts['1949']
ts_log = np.log(ts)
test_stationarity.draw_ts(ts_log)
test_stationarity.draw_trend(ts_log, 12)
diff_12 = ts_log.diff(12)
diff_12.dropna(inplace=True)
diff_12_1 = diff_12.diff(1)
diff_12_1.dropna(inplace=True)
test_stationarity.testStationarity(diff_12_1)
print(test_stationarity.testStationarity(diff_12_1))
decomposition = seasonal_decompose(ts_log, model="additive")
trend = decomposition.trend
seasonal = decomposition.seasonal
residual = decomposition.resid