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import numpy as np
from statsmodels.graphics.tsaplots import acf, pacf
import pandas as pd
import streamlit as st
def create_standard_acf_array(data_series):
return acf(data_series, alpha=0.05)
def create_standard_pacf_array(data_series):
return pacf(data_series, alpha=0.05)
def calculate_corr_significance_intervals(corr_array):
lower = corr_array[1][:, 0] - corr_array[0]
upper = corr_array[1][:, 1] - corr_array[0]
return lower, upper
def corr_significance_analysis(significance_values, acf_array):
corr_significance_check = abs(acf_array) - abs(significance_values)
signficant_non_zero_index = np.where(corr_significance_check > 0)
significant_non_zero_correlations = acf_array[signficant_non_zero_index]
df = pd.DataFrame(
{'Lag': np.array(signficant_non_zero_index).squeeze(),
'Significant non-zero autocorrelation value': significant_non_zero_correlations,
'Autocorrelation absolute value': np.abs(significant_non_zero_correlations)
})
significant_non_zero_correlations_count = df.shape[0]
return df, significant_non_zero_correlations_count
def corr_analysis(corr_array):
lower, upper = calculate_corr_significance_intervals(
corr_array)
corr_array_1 = corr_array[0]
df, significant_non_zero_correlations_count = corr_significance_analysis(
lower, corr_array_1)
return corr_array_1, df, significant_non_zero_correlations_count
def acf_settings():
with st.expander('ACF Settings:'):
acf_nlags_selected_col, acf_confidence_selected_col = st.columns(2)
with acf_nlags_selected_col:
acf_nlags_selected = st.number_input(
'Number of non-zero lags:', key='acf_nlags_selected', value=1)
with acf_confidence_selected_col:
confidence_interval = st.slider(
'Confidence interval (%)', min_value=0, max_value=99, value=95)
acf_adjust_selected_col, acf_fft_selected_col = st.columns(2)
with acf_adjust_selected_col:
acf_adjust_selected = st.radio(
'Adjusted:', ('False', 'True'), key='acf_adjust_selected')
with acf_fft_selected_col:
acf_fft_selected = st.radio(
'Compute ACF via FFT:', ('True', 'False'), key='acf_fft_selected')
acf_adjust_selected = acf_adjust_selected == 'True'
acf_fft_selected = acf_fft_selected == 'True'
return [confidence_interval,
acf_nlags_selected,
acf_fft_selected,
acf_adjust_selected]
pacf_calculation_methods = [
# Yule-Walker with sample-size adjustment in denominator for acovf. Default.
'yw', 'ywadjusted',
# Yule-Walker without adjustment. Default.
'ywm', 'ywmle',
# regression of time series on lags of it and on constant.
'ols',
# regression of time series on lags using a single common sample to estimate all pacf coefficients.
'ols-inefficient',
# regression of time series on lags with a bias adjustment.
'ols-adjusted',
# Levinson-Durbin recursion with bias correction.
'ld', 'ldadjusted',
# Levinson-Durbin recursion without bias correction.
'ldb', 'ldbiased']
def pacf_settings():
with st.expander('PACF Settings:'):
pacf_nlags_selected_col, pacf_confidence_selected_col = st.columns(2)
with pacf_nlags_selected_col:
pacf_nlags_selected = st.number_input(
'Number of non-zero lags:', key='ppacf_nlags_selected', value=1)
with pacf_confidence_selected_col:
confidence_interval = st.slider(
'Confidence interval (%)', min_value=0, max_value=99, value=95, key='pacf_confidence_selected_col')
pacf_calculation_method = st.selectbox(
label='Method for calculation', options=pacf_calculation_methods)
return [confidence_interval,
pacf_nlags_selected,
pacf_calculation_method]
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