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Time Series Analysis and Forecasting using Python

Learn the concepts of Time Series Analysis and Forecasting

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Time Series Analysis and Forecasting using Python

₹799

  • 61 lessons
  • 0 students enrolled
  • English
  • Beginner

About this course

In this comprehensive Time Series Analysis and Forecasting course, you'll learn everything you need to confidently analyze time series data and make accurate predictions. Through a combination of theory and practical examples, in just 14-15 hours, you'll develop a strong foundation in time series concepts and gain hands-on experience with various models and techniques.

This course includes:

  • Understanding Time Series: Explore the fundamental concepts of time series analysis, including the different components of time series, such as trend, seasonality, and noise.
  • Decomposition Techniques: Learn how to decompose time series data into its individual components to better understand its underlying patterns and trends.
  • Autoregressive (AR) Models: Dive into autoregressive models and discover how they capture the relationship between an observation and a certain number of lagged observations.
  • Moving Average (MA) Models: Explore moving average models and understand how they can effectively smooth out noise and reveal hidden patterns in time series data.
  • ARIMA Models: Master the widely used ARIMA models, which combine the concepts of autoregressive and moving average models to handle both trend and seasonality in time series data.
  • Facebook Prophet: Get hands-on experience with Facebook Prophet, a powerful open-source time series forecasting tool, and learn how to leverage its capabilities to make accurate predictions.
  • Real-World Projects: Apply your knowledge and skills to three real-world projects, where you'll tackle various time series analysis and forecasting problems, gaining valuable experience and confidence along the way.

By the end of this course, you'll have a solid understanding of time series analysis and forecasting, as well as the ability to apply different models and techniques to solve real-world problems. Join us now and unlock the power of time series data to make informed predictions and drive business decisions. Enroll today and start your journey toward becoming a time series expert!

Curriculum

8 sections · 61 lessons

  • Introduction to Time Series Analysis
  • Time Series Vs Regression
  • Time Series Analysis

  • Anomaly Detection
  • Component of Time Series
  • Decompostion
  • Implementation of Decompostion
  • Additive and Multipliicaion Decompostion
  • Stationarity
  • Testing Time Series Staionarity
  • Transformation

  • Introduction to Pre-Processing
  • Handle Missing Value
  • Implementation of Feature Encoding
  • Implementation of Handle Missing value in Py
  • Outlier Treatment
  • Sigma Technique (Standard Deviation)
  • Feature Scaling
  • Feature Scaling Technique (Standardization)
  • Feature Scaling Technique (Normalization)
  • Implementation of Feature Scaling
  • Feature Encoding

  • Learn EDA from Scratch

  • Implementation of ARIMA
  • ARIMA [part 1]
  • Algorithms
  • Decompostion
  • Auto Correlation vs Partical Auto Correlation
  • Choosing the best transformation
  • Grid Search [part 1]
  • Grid Search [part 2]
  • Final Model
  • FBProphet [part 1]
  • FBProphet [part 2]
  • FBProphet [part 3]
  • ARIMA [part 2]
  • AR Theory
  • Ma Theory
  • Auto-Correlation Function (ACF) &Partical Auto-Correlation Function (PACF)
  • Find PDQ
  • ARIMA [practicals 1]
  • ARIMA [practicals 2]

  • Multi Variate TS Analysis
  • FB Prophet Uni & Multi Variate

  • Introduction
  • Forecasting Evaluation Metrics
  • Mean Squarred Error
  • Root Mean Sqaured Error
  • Mean Absolute Percentage Error

  • Project 1 - Energy Demand Forecasting [part 1]
  • Project 1 - Energy Demand Forecasting [part 2]
  • Project 1 - Energy Demand Forecasting [part 3]
  • Project 2 - Stock Market Prediction [part 1]
  • Project 2 - Stock Market Prediction [part 2]
  • Project 2 - Stock Market Prediction [part 3]
  • Project 3 - Demand Forecasting [part 1]
  • Project 3 - Demand Forecasting [part 2]
  • Project 3 - Demand Forecasting [part 3]
  • Project 3 - Demand Forecasting [part 4]
  • Project 3 - Demand Forecasting [part 5]
  • Project 3 - Demand Forecasting [part 6]
Time Series Analysis and Forecasting