Time Series Analysis And Forecasting Using Python 2024

ARIMA,Neural Prophet,LightGBM, Random Forest,Pandas,Lag-Llama,Conformal Predictions, Change points, Trend, Seasonality,

What you'll learn

Time Series Data Fundamentals : Reading and Importing Time Series Data

Exploratory Data Analysis with Time Series Data (Interactive Visualization of Time-Series Data)

Decomposition of Time Series Data into Trend, Seasonality Effects, Effect of change points

Detecting Stationarity in Time Series Data, Auto-Correlation Effects (ACF and PACF Plots)

Time Series Forecasting using Neural Prophet

Univariate Time Series Forecasting - ARIMA

Tree Based Time Series Forecasting - LightGBM

Fundamentals of Conformal Predictions in Time Series Forecasting (Random Forest, EnbPI)

Lag-Llama For Time Series Forecasting

Requirements

A basic knowledge of data science and ML principles could be helpful

Description

This course delves into the fundamental aspects of time series analysis and forecasting. This course has subsections on exploratory data analysis, decomposition of a time series into trend and seasonality components, neural prophet model, ARIMA, time series forecasting using supervised machine learning (tree-based model), fundamentals of conformal predictions and Lag-Llama model for zero shot learning to make forecast predictions. The first segment (section 2) covers the definition of time series, importing and reading time series data using SQL Alchemy and Pandas, converting from long-form to wide-form time series data, DarTS time series class and a basic example of exponential smoothing using DarTS.The second segment (section 3) explains the structure of time series - trend, seasonality components and change points, investigating scenarios related to trend, seasonality, auto-regressive effects and change points using the Neural Prophet Model to make forecast predictions with detailed references for further reading.The third segment (Section 4) delves into ARIMA model, analysis of stationarity effects using ADF test, Auto-Correlation and Partial Auto-Correlation function in Time Series and Akaike Information Criterion to select ARIMA model parameters for making forecast predictions.The fourth segment (Section 5) covers time series analysis and forecasting using supervised machine learning, creation of lagged features for a time series forecasting model and the use of Light Gradient Boosting Machine (Light GBM) for time series analysis and forecasting.The subsequent segment (Section 6) covers the fundamentals of conformal predictions in time series forecasting, defining exchangeability hypothesis, EnbPI algorithm as a conformal predictions framework together with random forest regressor and calculation of coverage score.The segment six (section 7) covers Lag-Llama which is an open source foundational model for time series forecasting.Each segment has a google colab notebook associated with it.

Overview

Section 1: Introduction

Lecture 1 Time Series Analysis and Forecasting using Python - Introductory Segment

Section 2: Time Series Data - Fundamentals

Lecture 2 Time Series Data and Data Generating Process

Lecture 3 Read, Import and Analyze Time Series Data - SQLAlchemy, Pandas

Lecture 4 Long-Form and Wide-Form Time Series Data

Lecture 5 DarTS for time series analysis and Preliminary Data Visualizations

Lecture 6 Lecture 6 : Basic Example of Exponential Smoothing using DarTS

Section 3: Structure of Time Series - Trend, Seasonality and Change Points

Lecture 7 Composition of time series - Trend, Seasonality and Change point detection

Lecture 8 Set up Google Colab notebook for the analysis of trend and seasonality effects

Lecture 9 Investigate scenarios related to Trend, Seasonality Effects and Change points

Lecture 10 Investigate scenarios related to Auto-Regressive effects in Neural Prophet

Lecture 11 Investigate Effects of Covariates on the forecast predictions in Neural Prophet

Section 4: Autoregressive Integrated Moving Average

Lecture 12 Introductory segment on ARIMA

Lecture 13 Analysis of Stationarity Effects in Time Series (Statistical test : ADF)

Lecture 14 Auto-Correlation Function and Partial Auto-Correlation Function in Time Series

Lecture 15 Akaike Information Criterion : ARIMA Model (differencing, MA and AR parameters)

Section 5: Time Series Forecasting using Supervised Machine Learning

Lecture 16 Introduction to Time Series Forecasting using Supervised Machine Learning

Lecture 17 Setting up the Google Colab notebook and Extracting Date Related Features

Lecture 18 Creation of Lagged Features for a Time Series Forecasting model

Lecture 19 Tree Based Time Series Forecasting using LightGBM

Section 6: Fundamentals of Conformal Predictions in Time Series Forecasting

Lecture 20 Conformal Predictions in Time Series Forecasting - Introductory Segment

Lecture 21 Exchangeability Hypothesis and Ensemble Batch Prediction Intervals

Lecture 22 EnbPI Algorithm Explanation and Setting up Google Colab Notebook

Lecture 23 Random Forest Regressor, Mapie Time Series Regressor and Coverage Score

Section 7: Lag-Llama For Time-Series Forecasting

Lecture 24 Introductory Segment on Lag-Llama Model

Lecture 25 Applying Language Model such as Lag-Llama for Time Series Forecasting

Lecture 26 Zero Shot Generalization capability of the Lag-Llama model & Set up Google Colab

Lecture 27 Forecast Predictions and CRPS Evaluation Metric for the Lag-Llama Model

This course is suited for anyone interested in delving into the realm of Time Series Analysis and Forecasting.