Welcome to Time Series Analysis
Time series data tracks changes over time, making it crucial for forecasting weather, predicting stock prices, analyzing business trends, and more. The Pandas library in Python is a game-changer for handling this type of data, thanks to its powerful and intuitive handling of sequences over time.
Getting Started: Loading Your Data
First things first, let's load some time series data. We'll use a dataset of daily female births to understand how to work with dates and times effectively in Pandas.
from pandas import read_csv
series = read_csv('daily-total-female-births.csv', header=0, index_col=0, parse_dates=True, squeeze=True)
We've just loaded our dataset as a Pandas Series, with dates parsed and set as the index—perfect for time series analysis!
Diving Deeper: Exploring and Visualizing Data
Once our data is loaded, the next step is exploration. Let's take a peek at the first few entries and then review the statistical summary to understand our dataset's distribution.
print(series.head())
print(series.describe())
With our data neatly loaded, it's time to visualize it to uncover any underlying patterns and trends.
Visualizing Time Series Data
import matplotlib.pyplot as plt
series.plot()
plt.show()
This simple plot can reveal a lot about the nature and variability of the data, helping us make informed decisions about further analysis.
Feature Engineering for Time Series
In time series analysis, feature engineering is vital. We create features that help us leverage the temporal dynamics of the data, like rolling averages or time lags, which are crucial for forecasting models.
Let's demonstrate creating an expanding window feature, which cumulatively calculates statistics over the data as it becomes available:
from pandas import DataFrame, concat
temps = DataFrame(series.values)
window = temps.expanding()
dataframe = concat([window.min(), window.mean(), window.max(), temps.shift(-1)], axis=1)
dataframe.columns = ['min', 'mean', 'max', 't+1']
This technique highlights trends over time and can be pivotal in understanding long-term changes.
Mastering Visualization Techniques
Effective visualization helps to communicate the stories hidden within our data. We'll explore various techniques:
from matplotlib import pyplot
series.hist()
pyplot.show()
series.plot(kind='kde')
pyplot.show()
These plots offer insights into the distribution and density of our data, useful for both analysis and presentation.
Advanced Techniques: Resampling and Power Transforms
Resampling is a powerful technique that changes the frequency of your time series data. This can be essential for aligning time series with different intervals, or for changing the granularity of analysis.
from scipy.stats import boxcox
series, lam = boxcox(series)
Here, we've applied a Box-Cox transform to stabilize variance, which is often necessary before applying statistical models.
The Power of Moving Average Smoothing
Moving average smoothing is a simple yet effective technique for reducing noise and highlighting trends in time series data.
from numpy import mean
window = 3
moving_averages = [mean(series[i-window:i]) for i in range(window, len(series))]
This approach smooths out short-term fluctuations and highlights longer-term trends or cycles.