解释这段代码from statsmodels.tsa.seasonal import seasonal_decompose def decompose(timeseries):
时间: 2024-06-11 17:07:22 浏览: 8
这段代码定义了一个函数`decompose`,该函数使用了`statsmodels`模块中的`seasonal_decompose`函数来进行时间序列的季节性分解。
函数`decompose`接收一个时间序列作为参数,返回一个元组,包含原始时间序列的趋势、季节和残差部分。该函数的具体实现如下:
```python
from statsmodels.tsa.seasonal import seasonal_decompose
def decompose(timeseries):
# 使用seasonal_decompose函数对时间序列进行季节性分解
decomposition = seasonal_decompose(timeseries)
# 将分解结果保存在元组中,并返回
trend = decomposition.trend
seasonal = decomposition.seasonal
residual = decomposition.resid
return trend, seasonal, residual
```
在函数体内,首先使用`seasonal_decompose`函数对传入的时间序列进行季节性分解,并将分解结果保存在`decomposition`变量中。然后,从`decomposition`中分别获取趋势、季节和残差部分,并将它们保存在变量`trend`、`seasonal`和`residual`中。最后,将这三个变量打包成一个元组,并通过`return`语句返回。
相关问题
res = sm.tsa.seasonal_decompose(temp, period=1440, model="add")
This code uses the seasonal_decompose function from the statsmodels.tsa module to perform seasonal decomposition on the time series data stored in the temp variable. The period parameter is set to 1440, which indicates that the data has a seasonal cycle of 1440 time units (e.g. minutes, hours, days). The model parameter is set to "add", which indicates that the seasonal component is additive rather than multiplicative. The function returns a DecomposeResult object that contains the trend, seasonal, and residual components of the time series data.
from flask import Flask, request, jsonify import numpy as np import pandas as pd import statsmodels.api as sm from datetime import datetime app = Flask(name) @app.route('/time_series_analysis', methods=['POST']) def time_series_analysis(): # 解析请求体中的参数 arr0 = ['2019/1', '2019/2', '2019/3', '2019/4', '2019/5', '2019/6', '2019/7', '2019/8', '2019/9', '2019/10', '2019/11', '2019/12', '2020/1', '2020/2', '2020/3', '2020/4', '2020/5', '2020/6', '2020/7', '2020/8', '2020/9', '2020/10', '2020/11', '2020/12'] date_arr = [] for date_str in arr0: date_obj = datetime.strptime(date_str, '%Y/%m') date_arr.append(date_obj.timestamp()) arr1 = np.array(request.json['data'], dtype=float) data_array = np.vstack((date_arr, arr1)).T.astype(float) df = pd.DataFrame(data_array, columns=['x', 'y']) df = df.dropna() acf, q, p = sm.tsa.acf(df['y'], nlags=20, qstat=True) if (p < 0.05).any(): short_term_dependency = True else: short_term_dependency = False acf, q, p = sm.tsa.acf(df['y'], nlags=20, fft=True, qstat=True) if (p < 0.05).any(): periodicity = True else: periodicity = False adf_result = sm.tsa.stattools.adfuller(df['y']) if adf_result[1] < 0.05: stationary = True else: stationary = False res = sm.tsa.seasonal_decompose(df['y'], model='additive', period=12) if np.isnan(res.seasonal).any(): seasonality = False else: seasonality = True # 返回分析结果 result = { 'short_term_dependency': short_term_dependency, 'periodicity': periodicity, 'stationary': stationary, 'seasonality': seasonality, 'recommendations': 'arima擅长处理平稳数据,其他数据处理效果也很好\nlightGBM擅长处理短期依赖型,非周期性的数据\nLSTM擅长处理长期依赖的数据\nTNC擅长处理没有明显的周期性或季节性变化,但是可能存在趋势和周期性的波动的数据\nRNN由于具有记忆能力,可以处理所有数据' } return jsonify(result) if name == 'main': app.run(),做修改能显示出实际的接口网站
你需要将name改为__name__,并修改app.run()为app.run(debug=True)。这样可以启动调试模式,方便查看错误信息。同时,你需要确保已经安装了Flask和其他依赖库。最后,在运行程序之前,确保端口没有被占用。以下是修改后的代码:
```
from flask import Flask, request, jsonify
import numpy as np
import pandas as pd
import statsmodels.api as sm
from datetime import datetime
app = Flask(__name__)
@app.route('/time_series_analysis', methods=['POST'])
def time_series_analysis():
# 解析请求体中的参数
arr0 = ['2019/1', '2019/2', '2019/3', '2019/4', '2019/5', '2019/6', '2019/7', '2019/8', '2019/9', '2019/10', '2019/11', '2019/12', '2020/1', '2020/2', '2020/3', '2020/4', '2020/5', '2020/6', '2020/7', '2020/8', '2020/9', '2020/10', '2020/11', '2020/12']
date_arr = []
for date_str in arr0:
date_obj = datetime.strptime(date_str, '%Y/%m')
date_arr.append(date_obj.timestamp())
arr1 = np.array(request.json['data'], dtype=float)
data_array = np.vstack((date_arr, arr1)).T.astype(float)
df = pd.DataFrame(data_array, columns=['x', 'y'])
df = df.dropna()
acf, q, p = sm.tsa.acf(df['y'], nlags=20, qstat=True)
if (p < 0.05).any():
short_term_dependency = True
else:
short_term_dependency = False
acf, q, p = sm.tsa.acf(df['y'], nlags=20, fft=True, qstat=True)
if (p < 0.05).any():
periodicity = True
else:
periodicity = False
adf_result = sm.tsa.stattools.adfuller(df['y'])
if adf_result[1] < 0.05:
stationary = True
else:
stationary = False
res = sm.tsa.seasonal_decompose(df['y'], model='additive', period=12)
if np.isnan(res.seasonal).any():
seasonality = False
else:
seasonality = True
# 返回分析结果
result = {
'short_term_dependency': short_term_dependency,
'periodicity': periodicity,
'stationary': stationary,
'seasonality': seasonality,
'recommendations': 'arima擅长处理平稳数据,其他数据处理效果也很好\nlightGBM擅长处理短期依赖型,非周期性的数据\nLSTM擅长处理长期依赖的数据\nTNC擅长处理没有明显的周期性或季节性变化,但是可能存在趋势和周期性的波动的数据\nRNN由于具有记忆能力,可以处理所有数据'
}
return jsonify(result)
if __name__ == '__main__':
app.run(debug=True)
```
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