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Time Series Analysis And Forecasting Using Python (2024) - OneDDL - 06-09-2024 Free Download Time Series Analysis And Forecasting Using Python (2024) Published 6/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.60 GB | Duration: 2h 7m 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. 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