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خود همبستگی(Auto correlation) سری زمانی
در داده هاي سري زماني، مشکل شايع خود همبستگي يا همبستگي پياپي است كه بر روي جملات اخلال در دوره هاي مختلف وجود دارد.
يکي از دلايل وجود همبستگي پياپي يا خود همبستگي اين است كه عوامل موثر از سري زماني حذف شده است، عواملي كه مانند متغيرهاي موجود در مدل مهم و با يکديگر ارتباط دارند.
import matplotlib.pyplot as plot
import numpy as np
# Time series data
data = np.array([24.40,10.25,20.05,22.00,16.90,7.80,15.00,22.80,34.90,13.30])
plot.acorr(data, maxlags=9)
plot.title('Autocorrelation of XYZ stock price data')
plot.xlabel('Lag')
plot.ylabel('Autocorrelation')
plot.show()
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#همبستگی_سری_زمانی
#سری_زمانی

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خود همبستگی(Auto correlation) سری زمانی
در داده هاي سري زماني، مشکل شايع خود همبستگي يا همبستگي پياپي است كه بر روي جملات اخلال در دوره هاي مختلف وجود دارد.
يکي از دلايل وجود همبستگي پياپي يا خود همبستگي اين است كه عوامل موثر از سري زماني حذف شده است، عواملي كه مانند متغيرهاي موجود در مدل مهم و با يکديگر ارتباط دارند.
import matplotlib.pyplot as plot
import numpy as np
# Time series data
data = np.array([24.40,10.25,20.05,22.00,16.90,7.80,15.00,22.80,34.90,13.30])
plot.acorr(data, maxlags=9)
plot.title('Autocorrelation of XYZ stock price data')
plot.xlabel('Lag')
plot.ylabel('Autocorrelation')
plot.show()
#پایتون_مالی
#همبستگی_سری_زمانی
#سری_زمانی

پایتون برای مالی در تلگرام https://www.tg-me.com/ca/Python4Finance/com.python4finance
پایتون برای مالی در بله https://ble.im/ca/Python4Finance/com.python4finance

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Importantly, that investor viewpoint is not new. It cycles in when conditions are right (and vice versa). It also brings the ineffective warnings of an overpriced market with it.Looking toward a good 2022 stock market, there is no apparent reason to expect these issues to change.

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