Time Series Analysis ⏰
This code helps you to understand the Machine Learning basics and TSA concept.
Code Requirements 🦄
The example code is in Python (version 2.7 or higher will work). You need to install numpy, scikitlearn, pandas, matplotlib libraries.
You can install Conda for python which resolves all the dependencies for machine learning.
Description ⌛
A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called "time series analysis", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.
For more information, see
Results 📊
Execution 🐉
To run the code, type python Crimerate_Prediction.py
python Crimerate_Prediction.py