- Time Series Tutorial
- Time Series - Home
- Time Series - Introduction
- Time Series - Programming Languages
- Time Series - Python Libraries
- Data Processing & Visualization
- Time Series - Modeling
- Time Series - Parameter Calibration
- Time Series - Naive Methods
- Time Series - Auto Regression
- Time Series - Moving Average
- Time Series - ARIMA
- Time Series - Variations of ARIMA
- Time Series - Exponential Smoothing
- Time Series - Walk Forward Validation
- Time Series - Prophet Model
- Time Series - LSTM Model
- Time Series - Error Metrics
- Time Series - Applications
- Time Series - Further Scope
- Time Series Useful Resources
- Time Series - Quick Guide
- Time Series - Useful Resources
- Time Series - Discussion

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A basic understanding of any programming language is essential for a user to work with or develop machine learning problems. A list of preferred programming languages for anyone who wants to work on machine learning is given below −

It is a high-level interpreted programming language, fast and easy to code. Python can follow either procedural or object-oriented programming paradigms. The presence of a variety of libraries makes implementation of complicated procedures simpler. In this tutorial, we will be coding in Python and the corresponding libraries useful for time series modelling will be discussed in the upcoming chapters.

Similar to Python, R is an interpreted multi-paradigm language, which supports statistical computing and graphics. The variety of packages makes it easier to implement machine learning modelling in R.

It is an interpreted object-oriented programming language, which is widely famous for a large range of package availability and sophisticated data visualization techniques.

These are compiled languages, and two of the oldest programming languages. These languages are often preferred to incorporate ML capabilities in the already existing applications as they allow you to customize the implementation of ML algorithms easily.

MATrix LABoratory is a multi-paradigm language which gives functioning to work with matrices. It allows mathematical operations for complex problems. It is primarily used for numerical operations but some packages also allow the graphical multi-domain simulation and model-based design.

Other preferred programming languages for machine learning problems include JavaScript, LISP, Prolog, SQL, Scala, Julia, SAS etc.

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