【发布时间】：2020-07-27 06:09:00
【问题描述】：
我是时间序列机器学习的初学者，我需要开发一个项目，我的数据由分钟组成，有人可以帮我创建这个算法吗？
数据集：每个值代表一分钟的采集（9:00, 9:01 ...），采集持续10分钟，2个月进行一次，即10个值一月的值和二月的 10 个值。
目标：我希望我的结果是对 3 月份接下来 10 分钟的预测，例如：
2020-03-01 9:00:00
2020-03-01 9:01:00
2020-03-01 9:02:00
2020-03-01 9:03:00
培训：培训必须包含一月和二月作为预测的参考，同时考虑到它是一个时间序列
季节性：
预测：
当前问题：当前的预测似乎失败了， 以前的数据似乎不是有效的时间序列，因为， 可以在季节性图像中看到，数据集显示为 直线。预测由图中的绿线表示 下图，和蓝线的原始数据，但是当我们 看到日期轴一直到 2020-11-01，它应该一直到 2020-03-01，另外原始数据在 图表
script.py
# -*- coding: utf-8 -*-
try:
import pandas as pd
import numpy as np
import pmdarima as pm
#%matplotlib inline
import matplotlib.pyplot as plt
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.seasonal import seasonal_decompose
from dateutil.parser import parse
except ImportError as e:
print("[FAILED] {}".format(e))
class operationsArima():
@staticmethod
def ForecastingWithArima():
try:
# Import
data = pd.read_csv('minute.csv', parse_dates=['date'], index_col='date')
# Plot
fig, axes = plt.subplots(2, 1, figsize=(10,5), dpi=100, sharex=True)
# Usual Differencing
axes[0].plot(data[:], label='Original Series')
axes[0].plot(data[:].diff(1), label='Usual Differencing')
axes[0].set_title('Usual Differencing')
axes[0].legend(loc='upper left', fontsize=10)
print("[OK] Generated axes")
# Seasonal
axes[1].plot(data[:], label='Original Series')
axes[1].plot(data[:].diff(11), label='Seasonal Differencing', color='green')
axes[1].set_title('Seasonal Differencing')
plt.legend(loc='upper left', fontsize=10)
plt.suptitle('Drug Sales', fontsize=16)
plt.show()
# Seasonal - fit stepwise auto-ARIMA
smodel = pm.auto_arima(data, start_p=1, start_q=1,
test='adf',
max_p=3, max_q=3, m=11,
start_P=0, seasonal=True,
d=None, D=1, trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
smodel.summary()
print(smodel.summary())
print("[OK] Generated model")
# Forecast
n_periods = 11
fitted, confint = smodel.predict(n_periods=n_periods, return_conf_int=True)
index_of_fc = pd.date_range(data.index[-1], periods = n_periods, freq='MS')
# make series for plotting purpose
fitted_series = pd.Series(fitted, index=index_of_fc)
lower_series = pd.Series(confint[:, 0], index=index_of_fc)
upper_series = pd.Series(confint[:, 1], index=index_of_fc)
print("[OK] Generated series")
# Plot
plt.plot(data)
plt.plot(fitted_series, color='darkgreen')
plt.fill_between(lower_series.index,
lower_series,
upper_series,
color='k', alpha=.15)
plt.title("ARIMA - Final Forecast - Drug Sales")
plt.show()
print("[SUCESS] Generated forecast")
except Exception as e:
print("[FAILED] Caused by: {}".format(e))
if __name__ == "__main__":
flow = operationsArima()
flow.ForecastingWithArima() # Init script
总结：
SARIMAX Results
================================================================================
Dep. Variable: y No. Observations: 22
Model: SARIMAX(0, 1, 0, 11) Log Likelihood nan
Date: Mon, 13 Apr 2020 AIC nan
Time: 21:19:10 BIC nan
Sample: 0 HQIC nan
- 22
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
intercept 0 5.33e-13 0 1.000 -1.05e-12 1.05e-12
sigma2 1e-10 5.81e-10 0.172 0.863 -1.04e-09 1.24e-09
===================================================================================
Ljung-Box (Q): nan Jarque-Bera (JB): nan
Prob(Q): nan Prob(JB): nan
Heteroskedasticity (H): nan Skew: nan
Prob(H) (two-sided): nan Kurtosis: nan
===================================================================================
【问题讨论】：
标签： python machine-learning time-series arima forecast