![]() PSD should also be calculated for a de-trended time series. append (, ignore_index = True ) return accuracy_df def plot_pgram ( series, diff_order ): """ This function plots thd Power Spectral Density of a de-trended series. array ( y1 ))) * 100, 1 ) accuracy_df = accuracy_df. round ( rmse ( y1, y2 ), 1 ) map_error = np. around ( resid_mean, 2 )) print ( " \n ** Ljung Box Test, p-value:", np. tight_layout () print ( "** Mean of the residuals: ", np. plot ( ax = ts_ax ) plot_acf ( residuals, lags = lags, ax = acf_ax ) sns. subplot2grid ( layout, ( 1, 1 )) residuals. subplot2grid ( layout, ( 1, 0 )) kde_ax = plt. subplot2grid ( layout, ( 0, 0 ), colspan = 2 ) acf_ax = plt. figure ( figsize = ( 10, 8 )) layout = ( 2, 2 ) ts_ax = plt. mean ( ljung ( x = residuals, lags = lags )) norm_p_val = jb ( residuals ) adfuller_p = adfuller ( residuals ) fig = plt. Lags should be min(2*seasonal_period, T/5) plots from: """ resid_mean = np. If Ljung Box test shows p> 0.05, the residuals as a group are white noise. Consider different model and/or add exogenous variable. If the first two are not met, we have not fully captured the information from the data for prediction. ![]() ![]() First two are must, while last two are good to have. Ideally the residuals should be uncorrelated, zero mean, constant variance and normally distributed. abs (( y_true - y_pred ) / y_true )) * 100 def residcheck ( residuals, lags ): """ Function to check if the residuals are white noise. If the series has outliers, compare/select model using MAPE instead of rmse() """ y_true, y_pred = np. #collapse-hide # Define some custom functions to help the analysis def MAPE ( y_true, y_pred ): """ %Error compares true value with predicted value. #statistics libraries import statsmodels.api as sm import scipy from scipy.stats import anderson from _measures import rmse from import adfuller from import month_plot, seasonal_plot, plot_acf, plot_pacf, quarter_plot from import seasonal_decompose from import ExponentialSmoothing, SimpleExpSmoothing from import acorr_ljungbox as ljung #from nimbusml.timeseries import SsaForecaster from import diff as diff import pmdarima as pm from pmdarima import ARIMA, auto_arima from scipy import signal from scipy.stats import shapiro from scipy.stats import boxcox from sklearn.preprocessing import StandardScaler #library to use R in Python import rpy2 from rpy2.robjects import pandas2ri pandas2ri. ![]() use ( 'seaborn-white' ) % matplotlib inline #collapse-hide #Author: Sandeep Pawar #Version: 1.0 #Date import pandas as pd import numpy as np import itertools #Plotting libraries import matplotlib.pyplot as plt import seaborn as sns import altair as alt plt. ![]()
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