Canadian-dollar_RMB

import pandas as pd

import matplotlib.pyplot as plt

import statsmodels as sm

from statsmodels.graphics.tsaplots import plot_acf,plot_pacf

import numpy as np

discfile = r'C:\Users\GitHung Zeng\Documents\WeChat Files\WT13662535567\Files\日线数据\Canadian-dollar_RMB.xlsx'

forecastnum = 5

data = pd.read_excel(discfile, index_col = '时间')

data = data['收']

plt.rcParams['font.sans-serif'] = ['SimHei']

##datetime时间转化

data.index = pd.to_datetime(data.index,format="%Y/%m/%d")

plt.figure(figsize=(8,4))

plt.plot(data)

plt.show()

##ACF,PACF计算

fig = plt.figure(figsize = (12,8))

ax1 = fig.add_subplot(211)

fig = plot_acf(data,lags = 40,ax =ax1)

ax1.xaxis.set_ticks_position('bottom')

fig.tight_layout()

ax2 = fig.add_subplot(212)

fig = plot_pacf(data,lags = 40,ax =ax2)

ax1.xaxis.set_ticks_position('bottom')

fig.tight_layout()

data_diff = data.diff(5)

data_diff = data_diff.dropna()

##ACF,PACF计算

fig = plt.figure(figsize = (12,8))

ax1 = fig.add_subplot(211)

fig = plot_acf(data_diff,lags = 40,ax =ax1)

ax1.xaxis.set_ticks_position('bottom')

fig.tight_layout()

ax2 = fig.add_subplot(212)

fig = plot_pacf(data_diff,lags = 40,ax =ax2)

ax1.xaxis.set_ticks_position('bottom')

fig.tight_layout()

arima11 = sm.tsa.arima_model.ARMA(data_diff,order=(6,4))

model_results=arima11.fit()

predict = model_results.predict()

forecast = model_results.forecast(5)

plt.figure(figsize=(12,8))

plt.plot(data_diff.iloc[data_diff.size-1000:data_diff.size])

plt.plot(predict[data_diff.size-1000:data_diff.size],'r')

plt.show()

forecast[1]