Time Series Analysis with Python

2.1 What is a Time Series?

A time series is a sequence of data points indexed in time order. Common examples include stock prices, temperature readings, and sales data.

2.2 Components of Time Series

• Trend: Long-term movement in data.
• Seasonality: Regular pattern of fluctuations.
• Cyclic: Fluctuations that occur at irregular intervals.
• Irradiation: Random noise or irregular variations.

2.3 Stationarity

A time series is stationary if its statistical properties (mean, variance) are constant over time. Many time series models require stationarity.

You will need the following Python libraries:

• Pandas
• Numpy
• Matplotlib
• Statsmodels
• Scikit-learn

Install them using pip:

pip install pandas numpy matplotlib statsmodels scikit-learn

Data preparation involves loading the dataset and performing cleaning and transformation as necessary.

import pandas as pd

# Load the dataset
data = pd.read_csv('path_to_your_file.csv', parse_dates=['date_column'], index_col='date_column')

# Check for missing values
data.isnull().sum()

Visualizing the time series data helps to identify patterns, trends, and seasonality.

import matplotlib.pyplot as plt

# Plot the time series data
plt.figure(figsize=(12, 6))
plt.plot(data)
plt.title('Time Series Data')
plt.xlabel('Date')
plt.ylabel('Values')
plt.show()

Decompose the time series data into its components to better understand the underlying patterns.

from statsmodels.tsa.seasonal import seasonal_decompose

# Decompose the time series
decomposition = seasonal_decompose(data, model='additive')
decomposition.plot()
plt.show()

Modeling the time series data can be done using various techniques like ARIMA, SARIMA, or machine learning approaches.

from statsmodels.tsa.arima.model import ARIMA

# Fit the ARIMA model
model = ARIMA(data, order=(5,1,0))
model_fit = model.fit()

# Summary of the model
print(model_fit.summary())