Time Series Analysis of Netflix Stocks with Pandas

Introduction

Time series analysis is a powerful technique for understanding patterns and trends in data collected over time. By studying historical data points in chronological order, we can gain valuable insights, make predictions about the future, and inform data-driven decision making.

Time series data is ubiquitous, found in domains ranging from finance and economics to weather forecasting and sales planning. In the realm of finance, time series analysis is particularly useful for analyzing stock prices, identifying trends, and potentially forecasting future stock performance.

In this article, we will embark on a journey to explore the fascinating world of time series analysis using the popular Python library, pandas. We will dive into a practical example by analyzing the stock prices of Netflix, a well-known streaming giant. Through this analysis, we will learn how to work with time series data, apply various techniques to gain insights, and even attempt to forecast future stock prices.

Netflix: A Streaming Powerhouse

Netflix has revolutionized the entertainment industry with its streaming service, offering a vast library of movies and TV shows to subscribers worldwide. Founded in 1997, the company has experienced tremendous growth and success over the years.

As a publicly traded company, Netflix‘s stock price (NFLX) has been a subject of interest for investors and analysts alike. The stock has witnessed significant fluctuations, reflecting the company‘s performance, market sentiment, and overall industry trends.

Importing Netflix Stock Data

To begin our analysis, we need to obtain historical stock price data for Netflix. We can accomplish this using the yfinance library in Python, which allows us to retrieve financial data from Yahoo Finance.

First, let‘s install the necessary libraries:

!pip install yfinance pandas matplotlib

Next, we can import the required libraries and download the Netflix stock data:

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

# Download Netflix stock data
netflix_df = yf.download(‘NFLX‘, start=‘2002-01-01‘, end=‘2023-06-09‘)

The yf.download() function retrieves the historical stock data for Netflix (NFLX) from January 1, 2002, to June 9, 2023. The resulting data is stored in a pandas DataFrame called netflix_df.

Visualizing Netflix Stock Price

Let‘s take a visual look at Netflix‘s stock price over time. We can use pandas and matplotlib to create a line plot of the closing price:

plt.figure(figsize=(12, 6))
plt.plot(netflix_df[‘Close‘])
plt.title(‘Netflix Stock Price‘)
plt.xlabel(‘Date‘)
plt.ylabel(‘Closing Price‘)
plt.show()

The resulting plot shows the progression of Netflix‘s stock price from 2002 to 2023. We can observe the overall upward trend, with some significant fluctuations along the way.

Time Series Analysis with Pandas

Now that we have the Netflix stock data loaded into a pandas DataFrame, let‘s explore some common techniques used in time series analysis.

Time Shifting

Time shifting, also known as lagging, involves shifting the time series data forward or backward by a specified number of periods. This can be useful for comparing values across different time points or calculating relative changes.

To shift the stock price data by one day forward, we can use the shift() function:

netflix_df[‘Close_lag1‘] = netflix_df[‘Close‘].shift(1)

This creates a new column called Close_lag1 that contains the closing price shifted by one day. We can compare the original and shifted prices to observe any patterns or relationships.

Rolling Window Calculations

Rolling window calculations allow us to compute various statistics over a sliding window of a specified size. This is useful for smoothing out short-term fluctuations and identifying trends.

Let‘s calculate the rolling mean of the closing price over a 30-day window:

netflix_df[‘Close_rolling_mean_30‘] = netflix_df[‘Close‘].rolling(window=30).mean()

The rolling() function creates a rolling window of size 30, and the mean() function calculates the average closing price within each window. This provides a smoother representation of the stock price trend.

Time Resampling

Time resampling involves aggregating or downsampling the time series data to a different frequency. This can help in analyzing the data at various granularities, such as daily, weekly, or monthly.

To resample the Netflix stock data to a monthly frequency and calculate the mean closing price, we can use the resample() function:

netflix_monthly = netflix_df.resample(‘M‘)[‘Close‘].mean()

This resamples the data to a monthly frequency (‘M‘) and calculates the mean closing price for each month. The resulting netflix_monthly Series contains the average monthly closing prices.

Forecasting Netflix Stock Price

In addition to analyzing historical stock prices, we can attempt to forecast future prices using time series models. Two popular models for time series forecasting are ARIMA (Autoregressive Integrated Moving Average) and Prophet (developed by Facebook).

Let‘s split our data into training and testing sets, build an ARIMA model, and evaluate its performance:

from statsmodels.tsa.arima.model import ARIMA

# Split data into train and test sets
train_data = netflix_df[‘Close‘][:len(netflix_df)-365]
test_data = netflix_df[‘Close‘][-365:]

# Build ARIMA model
model = ARIMA(train_data, order=(1, 1, 1))
model_fit = model.fit()

# Make predictions
predictions = model_fit.forecast(steps=len(test_data))

# Evaluate model performance
mse = ((predictions - test_data) ** 2).mean()
print(f"Mean Squared Error: {mse:.2f}")

In this example, we split the last 365 days of data as the test set and use the remaining data for training. We build an ARIMA model with parameters (1, 1, 1) and fit it to the training data. Then, we make predictions for the test set and calculate the Mean Squared Error (MSE) to assess the model‘s performance.

Conclusion

In this article, we explored the fascinating world of time series analysis using Netflix stock data and the pandas library. We learned how to import stock data using yfinance, visualize the stock price over time, and apply various techniques such as time shifting, rolling window calculations, and time resampling to gain insights from the data.

Furthermore, we delved into the realm of time series forecasting by building an ARIMA model to predict future Netflix stock prices. While forecasting stock prices is a complex and challenging task, this example demonstrates the potential of using time series models to make informed predictions.

Time series analysis is a powerful tool that can uncover hidden patterns, trends, and relationships in data collected over time. By leveraging the capabilities of pandas and other Python libraries, we can efficiently analyze and visualize time series data, enabling us to make data-driven decisions and gain a deeper understanding of the underlying dynamics.

As we conclude this exploration of Netflix stock analysis, it‘s important to remember that stock prices are influenced by numerous factors, including company performance, market conditions, and investor sentiment. While time series analysis can provide valuable insights, it should be used in conjunction with fundamental analysis and other relevant information to make well-informed investment decisions.

Happy analyzing and may your time series adventures be insightful!