Ultimate Enterprise Data Analysis and Forecasting Using Python: Leverage Cloud Platforms with Azure Time Series Insights and AWS Forecast Components for Deep Learning Modeling Usin by Pandian Shanthababu;

Author:Pandian, Shanthababu; [Pandian, Shanthababu]
Language: eng
Format: epub
Publisher: Orange Education PVT Ltd
Published: 2023-03-15T00:00:00+00:00

β is the smoothing factor [ value range from 0 to 1].

xt is the observation from the dataset during the time(t) period.

In the following calculation, the smoothing factor for the current and previous data points is 0.1 and 0.9:

Figure 6.41: EMA model output – predicted price along with formula

Figure 6.42 shows the actual vs forecast line plot using the EMA model.

Figure 6.42: EMA model line plot for diesel price dataset

EMA implementation using Python

Let’s implement the EMA model using Python code with a diesel data set and plot the line chart to understand the movements.

#load the required libraries

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from matplotlib import pyplot

#load dataset for EMA model analysis

df_diesel_price_EMA = pd.read_csv(“diesel_price EMA.csv”, header=0, index_col=0)

df_diesel_price_EMA.index.name = ‘Sale_Date’

df_diesel_price_EMA[‘average_price’] = df_diesel_price_EMA.mean(axis=1)

df_diesel_price_EMA[‘EMA’] = df_diesel_price_EMA.average_price.expanding().mean()

# smoothing factor - 0.1,0.25,.5

df_diesel_price_EMA[‘EMA_0.1’] = df_diesel_price_EMA.average_price.ewm(alpha=0.1, adjust=False).mean()

df_diesel_price_EMA[‘EMA_0.25’] = df_diesel_price_EMA.average_price.ewm(alpha=0.25, adjust=False).mean()

df_diesel_price_EMA[‘EMA_0.5’] = df_diesel_price_EMA.average_price.ewm(alpha=0.5, adjust=False).mean()

colors = [‘orange’,’blue’, ‘green’, ‘red’]

df_diesel_price_EMA[[‘average_price’, ‘EMA_0.1’,’EMA_0.25’,’EMA_0.5’]].plot(color=colors, linewidth=3, figsize=(12,6))

plt.xticks(fontsize=14)

plt.yticks(fontsize=14)

plt.legend(labels =[‘Average price’, ‘EMA_0.1’,’EMA_0.25’,’EMA_0.5’], fontsize=14)

plt.title(‘Diesel’, fontsize=20)

plt.xlabel(‘Sala_Date’, fontsize=16)

plt.ylabel(‘Diesel Price’, fontsize=16)

print(“ ############################################################”)

print(“ Exponential Moving Average Plot for Diesel “)

print(“ ############################################################”)

############################################################

Exponential Moving Average Plot for Diesel

############################################################

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