The Python ecosystem is the dominant platform for applied Machine Learning (ML). The primary rationale for adopting Python for time series forecasting is that it is a general-purpose programming language that you can use both for experimentation and production. It is easy to learn and use, primarily because the language focuses on readability. Python is a dynamic language and well suited to interactive development and quick prototyping, and with the power to support the development of large applications.
What is Time Series:
A time series is a sequence of observations over a certain period. A univariate time series consists of the values taken by a single variable at periodic time instances over a period, and a multivariate time series consists of the values taken by multiple variables at the same periodic time instances over a period. The simplest example of a time series that all of us come across on a day to day basis is the change in temperature throughout the day or week or month or year.
Python has an established popularity among individuals who perform machine learning because of its easy-to-write and easy-to-understand code structure as well as a wide variety of open source libraries.
Python Open Source Libraries for Time Series:
Here we have some open source libraries below.
Science Python is a library used for scientific and technical computing. It provides functionalities for optimization, signal and image processing, integration, interpolation and linear algebra. This library comes handy while performing machine learning. We will discuss these functionalities as we move ahead in this tutorial.
Numerical Python is a library used for scientific computing. It works on an N-dimensional array object and provides basic mathematical functionality such as size, shape, mean, standard deviation, minimum, maximum as well as some more complex functions such as linear algebraic functions and Fourier transform. You will learn more about these as we move ahead in this tutorial.
This library provides highly efficient and easy-to-use data structures such as series, dataframes and panels. It has enhanced Python’s functionality from mere data collection and preparation to data analysis. The two libraries, Pandas and NumPy, make any operation on small to very large dataset very simple. To know more about these functions, follow this tutorial.
4. Scikit Learn
This library is a SciPy Toolkit widely used for statistical modelling, machine learning and deep learning, as it contains various customizable regression, classification and clustering models. It works well with Numpy, Pandas and other libraries which makes it easier to use.
Like Scikit Learn, this library is used for statistical data exploration and statistical modelling. It also operates well with other Python libraries.
This library is used for data visualization in various formats such as line plot, bar graph, heat maps, scatter plots, histogram etc. It contains all the graph related functionalities required from plotting to labelling. We will discuss these functionalities as we move ahead in this tutorial.
These libraries are very essential to start with machine learning with any sort of data.
This library, with its two modules − datetime and calendar, provides all necessary datetime functionality for reading, formatting and manipulating time.
Python Open Source Framework for Time Series:
Kats is a toolkit for analyzing time series data, including a lightweight, easy-to-use, and generalizable framework for performing time series analysis. As I’ve discussed, time series analysis is an essential component of data science and engineering work in industry, from understanding key statistics and characteristics, detecting regressions and anomalies, to forecasting future trends. Kats aims to provide the one-stop shop for time series analysis, including detection, forecasting, feature extraction/embedding, multivariate analysis, and more.
Prophet is a framework for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust in regard to missing data and shifts in trend, and typically handles outliers well.
PyFlux is a library for time series analysis and prediction. Users can choose from a flexible range of modeling and inference options and use the output for forecasting and retrospection. Users can build a full probabilistic model where the data y and latent variables (parameters) z are treated as random variables through a joint probability p(y, z). The advantage of a probabilistic approach is that it gives a more complete picture of uncertainty, which is important for time series tasks such as forecasting. Alternatively, for speed, users can simply use Maximum Likelihood estimation for speed within the same unified API.
Sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes time series classification, regression, clustering, annotation, and forecasting. It comes with time series algorithms and scikit-learn–compatible tools to build, tune, and validate time series models.
Auto_TimeSeries is a complex model-building utility for time series data. Because it automates many tasks involved in a complex endeavor, it assumes many intelligent defaults — but you can change them. Auto_TimeSeries rapidly builds predictive models based on Statsmodels ARIMA, Seasonal ARIMA, and Scikit-Learn ML. It automatically selects the best model that gives the best score specified. Auto_TimeSeries enables you to build and select multiple time series models using techniques such as ARIMA, SARIMAX, VAR, decomposable (trend + seasonality + holidays) models, and ensemble Machine Learning models.
TimeSynth is an open source library for generating synthetic time series for model testing. The library can generate regular and irregular time series. The architecture allows the user to match different signals with different architectures allowing a vast array of signals to be generated. The available signals and noise types are listed below.
Tsfresh automatically calculates many time series characteristics, the so-called features. The package also contains methods to evaluate the explaining power and importance of such characteristics for regression or classification tasks.
Darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models and combine the predictions of several models and external regressors. Darts supports both univariate and multivariate time series and models. The neural networks can be trained on multiple time series, and some of the models offer probabilistic forecasts.
Orbit is a Python package for Bayesian time series forecasting and inference. It provides a familiar and intuitive initialize-fit-predict interface for time series tasks, while utilizing probabilistic programming languages under the hood.
Arrow is a Python library that offers a sensible and human-friendly approach to creating, manipulating, formatting, and converting dates, times, and timestamps. It implements and updates the datetime type, plugging gaps in functionality and providing an intelligent module API that supports many common creation scenarios. Simply put, it helps you work with dates and times with fewer imports and a lot less code.
Pastas is an open source Python package for processing, simulating, and analyzing hydrological time series (models). The object-oriented structure allows for the quick implementation of new model components. Time series models can be created, calibrated, and analyzed with just a few lines of Python code with the built-in optimization, visualization, and statistical analysis tools.
12. Flow Forecast:
Flow forecast is an open source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting edge concepts with interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series forecasting framework.
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