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Python Time series

Time series analysis is a critical method used in many industries such as finance, economics, ecology, and physical sciences. Time series data are essentially a sequence of data points indexed (or listed, or graphed) in time order. Analysis of time series is essential to understand the underlying sequence pattern, predict future trends, and make informed decisions.

Python, with its robust libraries and packages, has become a favorite tool for time series analysis due to its simplicity, flexibility, and wide variety of statistical and machine learning tools. In this blog post, we will explore some fundamental concepts of time series analysis and how to implement them in Python.

Key Libraries for Time Series Analysis in Python

Python offers multiple libraries for efficient time series analysis, including:

  1. Pandas: A foundational Python library for data analysis and manipulation. It provides data structures and functions needed to manipulate structured data, including functionality for manipulating numerical tables and time series data.

  2. Numpy: A library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

  3. Matplotlib: A plotting library used for 2D graphics in Python. It can be used to create scatter plots, bar plots, histograms, and much more.

  4. Statsmodels: A library built specifically for statistics. It is built on top of NumPy, SciPy, and matplotlib and provides the functionality to explore data, estimate statistical models, and perform statistical tests.

  5. SciKit-Learn: One of the most widely used machine learning libraries in Python, it provides simple and efficient tools for data analysis and modeling.

  6. Prophet: A powerful library for time series forecasting developed by Facebook. It is designed to handle the common features of business time series, such as seasonality and holidays.


Exploring Time Series Data with Python

Before diving into modeling, it’s essential to explore and understand your data. Pandas make the data wrangling process intuitive and straightforward. For example, to load a time series dataset:

import pandas as pd

# Load the dataset
df = pd.read_csv('data.csv')

# Parse the time series data
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)

To visualize the data, you can simply use the plot function from pandas that relies on matplotlib:

df.plot(figsize=(10, 5))

Time Series Analysis with Python

There are multiple ways to perform time series analysis, including but not limited to decomposition, forecasting, and time series regression.

Decomposition: This involves separating a time series into several distinct components – typically trend, seasonality, and noise.

The following snippet uses statsmodels to decompose a time series:

from statsmodels.tsa.seasonal import seasonal_decompose

# Perform additive decomposition
decomposed = seasonal_decompose(df['column'], model='additive')

# Plot each component

Forecasting: It’s about predicting future values of a time series.

A simple forecasting technique is the AutoRegressive Integrated Moving Average (ARIMA) model. Below is an example of ARIMA implemented in Python:

from statsmodels.tsa.arima.model import ARIMA

# Fit the ARIMA model
model = ARIMA(df['column'], order=(1, 1, 1))
model_fit =

# Forecast
forecast, std_err, conf_int = model_fit.forecast(steps=10)

Time Series Regression: Time series regression is a statistical method to predict a future response based on the response history (known as autoregressive terms) and the transfer of input history (known as moving average terms).

With Python’s Scikit-Learn library, time series regression can be implemented as follows:

from sklearn.linear_model import LinearRegression

# Define predictor and response variables
X = df['column1'].values.reshape(-1, 1)
y = df['column2'].values

# Train the model
model = LinearRegression(), y)

# Predict
predictions = model.predict(X)

More advanced topics

Machine Learning for Time Series Analysis

Machine learning offers various models to deal with time series data. Let’s take a look at two powerful methods: Long Short Term Memory (LSTM) models and Gated Recurrent Units (GRU).

Long Short-Term Memory (LSTM)

LSTM is a type of Recurrent Neural Network (RNN) architecture. It is designed to remember long-term dependencies in sequence data that standard RNNs can’t. LSTMs are useful when dealing with time series data due to their inherent nature of ‘remembering’ long term data.

You can use the Keras library, which provides a simple way to define and train LSTM models. Here’s how you can use an LSTM for time series forecasting:

from keras.models import Sequential
from keras.layers import LSTM, Dense

# Prepare your sequence data here...

# Define LSTM model
model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features)))
model.compile(optimizer='adam', loss='mse')

# Fit model, y, epochs=200, verbose=0)

In this example, we define a single hidden LSTM layer with 50 memory cells, followed by a fully connected Dense layer that outputs one value per prediction. The model uses the Mean Squared Error (MSE) loss function and the efficient Adam version of stochastic gradient descent.

Gated Recurrent Units (GRU)

GRU is another type of RNN that, like LSTM, is designed to adaptively capture dependencies of different time scales. However, GRUs use a simpler structure and often offer similar performance to LSTM with less computational complexity, which makes them an attractive option for time series analysis.

Here’s an example of how to use a GRU for time series forecasting with Keras:

from keras.models import Sequential
from keras.layers import GRU, Dense

# Prepare your sequence data here...

# Define GRU model
model = Sequential()
model.add(GRU(50, activation='relu', input_shape=(n_steps, n_features)))
model.compile(optimizer='adam', loss='mse')

# Fit model, y, epochs=200, verbose=0)

This code is very similar to the LSTM example. We just replace the LSTM layer with a GRU layer.


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Page last modified: 2023-08-02 08:43:15