Time Series Tutorial
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- Time Series - Discussion
- Time Series - Useful Resources
- Time Series - Quick Guide
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- Time Series - LSTM Model
- Time Series - Prophet Model
- Time Series - Walk Forward Validation
- Time Series - Exponential Smoothing
- Time Series - Variations of ARIMA
- Time Series - ARIMA
- Time Series - Moving Average
- Time Series - Auto Regression
- Time Series - Naive Methods
- Time Series - Parameter Calibration
- Time Series - Modeling
- Data Processing & Visualization
- Time Series - Python Libraries
- Time Series - Programming Languages
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Time Series - Programming Languages
Time Series - Programming Languages
A basic understanding of any programming language is essential for a user to work with or develop machine learning problems. A pst of preferred programming languages for anyone who wants to work on machine learning is given below −
Python
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 pbraries makes implementation of comppcated procedures simpler. In this tutorial, we will be coding in Python and the corresponding pbraries useful for time series modelpng will be discussed in the upcoming chapters.
R
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 modelpng in R.
Java
It is an interpreted object-oriented programming language, which is widely famous for a large range of package availabipty and sophisticated data visuapzation techniques.
C/C++
These are compiled languages, and two of the oldest programming languages. These languages are often preferred to incorporate ML capabipties in the already existing apppcations as they allow you to customize the implementation of ML algorithms easily.
MATLAB
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, Jupa, SAS etc.
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