younis-bash/Time-Series-Analysis-

this repository focused on Time Series Analysis, which involves analyzing data points recorded at different time intervals. The goal is to apply statistical tools and techniques to examine and understand the time series data. Let's begin our exploration.

Time Series Analysis

This notebook focuses on Time Series Analysis, a statistical approach to analyze and understand data points recorded at different time intervals. The notebook covers various concepts, techniques, and tools used in time series analysis. Let's delve into the contents of this notebook:

Table of Contents

Introduction to Time Series Analysis

- Overview of time series data
- Importance and applications of time series analysis

Types of Data

- Time series data
- Cross-sectional data
- Pooled data

Time Series Terminology

- Understanding key terms used in time series analysis
- Components of a time series (trend, seasonality, and noise)

Time Series Analysis

- Exploratory data analysis of time series
- Preprocessing and data cleaning techniques

Visualize the Time Series

- Plotting time series data
- Identifying patterns and trends

Patterns in a Time Series

- Trend analysis
- Seasonal patterns and cycles
- Irregular or random fluctuations

Additive and Multiplicative Time Series

- Understanding the difference between additive and multiplicative time series

Decomposition of a Time Series

- Breaking down a time series into its components (trend, seasonality, and residual)
- Methods for decomposition (moving averages, STL decomposition)

Stationary and Non-Stationary Time Series

- Definition of stationary time series
- The importance of stationarity in time series analysis

How to Make a Time Series Stationary

- Techniques for transforming a non-stationary time series into a stationary one
- Differencing, logarithmic transformation, and other methods

How to Test for Stationarity

- Introduction to unit root tests
- Augmented Dickey Fuller test (ADF Test)
- Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test
- Philips Perron test (PP Test)

Difference Between White Noise and a Stationary Series

- Understanding the characteristics of white noise and stationary series

Detrend a Time Series

- Methods to remove trend component from a time series
- Subtracting the line of best fit, time series decomposition, and mean subtraction

Deseasonalize a Time Series

- Techniques to remove seasonal patterns from a time series
- Moving averages, seasonal differencing, and seasonal index division

How to Test for Seasonality of a Time Series

- Approaches to identify the presence of seasonality in a time series
- Visual inspection and autocorrelation function (ACF) plot

Autocorrelation and Partial Autocorrelation Functions

- Definition and interpretation of autocorrelation and partial autocorrelation
- Computation and analysis of ACF and PACF plots
- Lag plots for visualizing autocorrelation

Granger Causality Test

- Using the Granger causality test to determine causal relationships between time series
- Interpreting the results of the test

Smoothening a Time Series

- Techniques to reduce noise and highlight trends in a time series
- Moving average, LOESS smoothing, and LOWESS smoothing

References

- List of resources and references used in this notebook

This notebook provides a comprehensive overview of time series analysis, covering various aspects from data preprocessing and visualization to decomposition, stationarity testing, seasonality detection, and more. It serves as a guide for analyzing