https://github.com/muhammadibrahim313/time-series-for-beginner-with-examples
https://github.com/muhammadibrahim313/time-series-for-beginner-with-examples
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/muhammadibrahim313/time-series-for-beginner-with-examples
- Owner: muhammadibrahim313
- Created: 2024-06-27T04:07:25.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-06-27T04:13:46.000Z (11 months ago)
- Last Synced: 2024-12-29T11:30:59.549Z (5 months ago)
- Language: Jupyter Notebook
- Size: 798 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
Dataset Link : https://www.kaggle.com/datasets/varpit94/tesla-stock-data-updated-till-28jun2021
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# Table of Contents
- [Introduction to Time Series](#introduction-to-time-series)
- [Definition and Significance](#definition-and-significance)
- [What is Time Series Data?](#what-is-time-series-data)
- [Why is it Important?](#why-is-it-important)
- [Examples](#examples)
- [Real-world Examples](#real-world-examples)
- [Finance](#finance)
- [Meteorology](#meteorology)
- [Economics](#economics)
- [Basic Concepts](#basic-concepts)
- [Components of Time Series](#components-of-time-series)
- [Trend](#trend)
- [Seasonality](#seasonality)
- [Cyclicity](#cyclicity)
- [Irregularity](#irregularity)
- [Types of Time Series](#types-of-time-series)
- [Stationary vs. Non-stationary](#stationary-vs-non-stationary)
- [Visualization Techniques](#visualization-techniques)
- [Plotting Time Series Data](#plotting-time-series-data)
- [Highlighting Trends and Seasonal Components](#highlighting-trends-and-seasonal-components)
- [Time Series Decomposition](#time-series-decomposition)
- [Additive and Multiplicative Models](#additive-and-multiplicative-models)
- [Additive Model](#additive-model)
- [Multiplicative Model](#multiplicative-model)
- [Decomposition Methods](#decomposition-methods)
- [Moving Averages](#moving-averages)
- [STL Decomposition](#stl-decomposition)
- [Stationarity and Differencing](#stationarity-and-differencing)
- [Stationarity](#stationarity)
- [What Makes a Time Series Stationary?](#what-makes-a-time-series-stationary)
- [Why is it Important for Analysis?](#why-is-it-important-for-analysis)
- [Testing for Stationarity](#testing-for-stationarity)
- [Augmented Dickey-Fuller (ADF) Test](#augmented-dickey-fuller-adf-test)
- [KPSS Test](#kpss-test)
- [Differencing](#differencing)
- [How Differencing Can Make a Series Stationary](#how-differencing-can-make-a-series-stationary)
- [Step-by-Step Example of Differencing](#step-by-step-example-of-differencing)
- [Smoothing Techniques](#smoothing-techniques)
- [Moving Averages](#moving-averages-1)
- [Simple Moving Average (SMA)](#simple-moving-average-sma)
- [Weighted Moving Average (WMA)](#weighted-moving-average-wma)
- [Exponential Moving Average (EMA)](#exponential-moving-average-ema)
- [Exponential Smoothing Methods](#exponential-smoothing-methods)
- [Simple Exponential Smoothing (SES)](#simple-exponential-smoothing-ses)
- [Holt’s Linear Trend Model](#holts-linear-trend-model)
- [Holt-Winters Seasonal Model](#holt-winters-seasonal-model)
- [Time Series Models](#time-series-models)
- [Autoregressive (AR) Models](#autoregressive-ar-models)
- [Moving Average (MA) Models](#moving-average-ma-models)
- [Autoregressive Moving Average (ARMA) Models](#autoregressive-moving-average-arma-models)
- [Autoregressive Integrated Moving Average (ARIMA) Models](#autoregressive-integrated-moving-average-arima-models)
- [Step-by-Step Model Building and Example](#step-by-step-model-building-and-example)
- [Seasonal ARIMA (SARIMA) Models](#seasonal-arima-sarima-models)
- [Step-by-Step Model Building and Example](#step-by-step-model-building-and-example-1)
- [State-space Models](#state-space-models)
- [Kalman Filter](#kalman-filter)
- [Model Evaluation and Selection](#model-evaluation-and-selection)
- [Criteria for Model Selection](#criteria-for-model-selection)
- [AIC, BIC](#aic-bic)
- [Cross-validation Techniques](#cross-validation-techniques)
- [Example of k-fold Cross-validation](#example-of-k-fold-cross-validation)
- [Model Diagnostics](#model-diagnostics)
- [Residual Analysis](#residual-analysis)
- [Advanced Time Series Models](#advanced-time-series-models)
- [GARCH Models](#garch-models)
- [Example of Volatility Forecasting](#example-of-volatility-forecasting)
- [VAR Models](#var-models)
- [Machine Learning Approaches](#machine-learning-approaches)
- [LSTM](#lstm)
- [PROPHET](#prophet)
- [Practical Applications](#pa)
- [ARIMA](#arima)
- [ANOMLY Detection](#anamoly)
- [Case Study](#cs)
- [LIbraries and Implentaions](#lob)
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## Check this notebook on kaggle : https://www.kaggle.com/code/muhammadibrahimqasmi/time-series-magic-your-ultimate-beginner-s-guide