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.

https://rapidgator.net/file/2114e45d778059b9e60bdef6a60d5d41/Time_Series_Analysis_and_Forecasting_Using_Python.part1.rar.html
https://rapidgator.net/file/d4da044334b0aa50dca53ecca07e273a/Time_Series_Analysis_and_Forecasting_Using_Python.part2.rar.html