Time Series Forecasting
This block will teach you how to predict future values based on the previously experimented values using Python.
Introduction to forecasting data
In this module, you will learn how to collect data and predict the future value of data focusing on its unique trends. This technique is known as Forecasting data.
Definition and properties of Time Series data
This module will teach you about the introduction of time series data and cover all the time-series properties.
Examples of Time Series data
You will learn some real-time examples of time series data in this module.
Features of Time Series data
You will learn some essential features of time series data in this module.
Essentials for Forecasting
In this module, you will go through all the essentials required to perform Forecasting of your data.
Missing data and Exploratory analysis
Exploratory Data Analysis, or EDA, is essentially a type of storytelling for statisticians. It allows us to uncover patterns and insights, often with visual methods, within data. In this module, you will learn the basics of EDA with an example.
Components of Time Series data
In this module, you will go through all the components required for Time-series data.
Naive, Average and Moving Average Forecasting
Naive Forecasting is the most basic technique to forecast your data like stock prices. Whereas, Moving Average Forecasting is a technique to predict future value based on past values.
Decomposition of Time Series into Trend, Seasonality and Residual
This module will teach you how to decompose the time series data into Trend, Seasonality and Residual.
Validation set and Performance Measures for a Time Series model
In this module, you will learn how to evaluate your machine learning models on time series data by measuring their performance and validation them.
Exponential Smoothing method
A time series forecasting method used for univariate data is known as the Exponential Smoothing method, one of the most efficient forecasting methods.
ARIMA stands for Auto Regression Integrated Moving Average and is used to forecast time series following a seasonal pattern and a trend. It has three key aspects, namely: Auto Regression (AR), Integration (I), and Moving Average (MA).