Time series analysis is an important aspect of data science, enabling the examination and forecasting of series of data points collected or recorded at specific intervals over time. From predicting stock prices to monitoring sensor data, time series analysis is able to provide valuable insights across many different domains.

This article introduces 5 helpful Python functions that can assist beginners and practitioners in performing effective time series analysis. By reading, understanding, and writing these functions yourself you can gain both practical skills and a deeper understanding of the time series process. Plus, keeping them handy for future reuse can help save time and organize your code.

Now let’s have a look at our 5 DIY functions.

1. Load Time Series Data

Before performing any analysis, you’ll have to load and preprocess your time series data. The Pandas library is a powerful tool for this task, offering robust data manipulation capabilities.

Here’s a simple function that loads time series data from a CSV file, parsing dates and setting the date column as the index. It relies fully on Pandas, is very short, and is also very reusable.

import pandas as pd

def load_time_series_data(file_path):
    df = pd.read_csv(file_path, parse_dates=True, index_col='date')
    return df

This function reads in a CSV file, ensuring that the date column is parsed as a datetime object, and sets it as the index for easy time-based operations.

2. Resample Time Series Data

Resampling is a key operation in time series analysis, allowing us to aggregate data over different time periods. This can help in smoothing out short-term fluctuations and highlighting longer-term trends or cycles.

The following function demonstrates how to resample time series data using a specified frequency.

def resample_time_series(df, frequency):
    resampled_df = df.resample(frequency).mean()
    return resampled_df

This above function uses the resample method from Pandas to aggregate the data at the specified frequency, computing the mean for each period.

3. Decompose Time Series

Time series decomposition involves breaking down a series into its constituent components: trend, seasonality, and residuals. The components resulting from this breakdown can provide insights into the underlying patterns of the data.

The statsmodels library provides tools for this decomposition. Here’s a function that performs the operation.

from statsmodels.tsa.seasonal import seasonal_decompose

def decompose_time_series(df, model='additive'):
    decomposition = seasonal_decompose(df, model=model)
    return decomposition

Using this function, you can decompose a time series into its various components, choosing between an additive or multiplicative model depending on the nature of your data.

4. Create Lag Features

Lag features are an essential time series modeling concept, offering predictive models information about past values that can improve forecasts.

Below is a function to create lagged features for a time series dataset.

def create_lag_features(df, lag):
    df['lagged'] = df['value'].shift(lag)
    return df

This function generates a new column containing values from a specified number of periods earlier (lag), which can be useful for model training and evaluation.

5. Plot Time Series Data

Visualizing time series data is important for identifying series trends and patterns. Matplotlib is a versatile library for creating plots in Python.

The following function effectively plots time series data housed in a Pandas dataframe.

import matplotlib.pyplot as plt

def plot_time_series(df):
    plt.figure(figsize=(10, 6))
    plt.plot(df.index, df['value'], label='Time Series Data')
    plt.title('Time Series Plot')
    plt.xlabel('Date')
    plt.ylabel('Value')
    plt.legend()
    plt.show()

This function generates a clear and informative plot of your time series data, aiding in the visual analysis of trends and anomalies.

Wrapping Up

We have shown 5 basic DIY Python functions designed to aid in mastering time series analysis. The tasks covered with these functions include loading data, resampling, decomposition, creating lag features, and plotting. Inspecting these functions, and coding them yourself, and then practicing their use by applying them to real-world datasets will enhance your data science skills and deepen your understanding of time series analysis. Becoming accustomed to writing and saving your own functions to your code toolkit for later reuse is a great way to learn and have tools handy for whatever job you come up against.

Until next time, keep on coding and learning.