Time Series Analysis and Forecasting with MS Excel
Published 10/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 43m | Size: 1.5 GB

Learn about a comprehensive framework of Time Series Analysis and Forecasting with MS Excel

What you'll learn
Learn Weighted Average, Exponential Moving Average Analysis and Regression
Simple Forecasting Methods, Simple and Multiple Regression
Time Series Decomposition and Exponential Smoothing
Methods of Forecasting and Steps in Forecasting
Requirements
Prior knowledge of Mathematics and statistics
Description
Time series analysis is a statistical method to analyse the past data within a given duration of time to forecast the future. It comprises of ordered sequence of data at equally spaced interval. To understand the time series data & the analysis let us consider an example. Consider an example of Airline Passenger data. It has the count of passenger over a period of time.
Ample of time series data is being generated from a variety of fields. And hence the study time series analysis holds a lot of applications. Let us try to understand the importance of time series analysis in different areas.
Field of Economics: Budget studies, census Analysis, etc.
Field of Finance: Widely used in the field of finance such as to understand the stock market fluctuations, yield management, understand the market volatility, etc.
Social Scientistà: Birth rates or death rates over a period of time and can come with the schemes in their interest.
Healthcare: An epidemiologist might be interested in knowing the number of people infected over the past years. Like in the current situation the researchers might be interested in knowing the people affected by the coronavirus over a period of time. Blood pressure traced over a period of time can be used in evaluating a drug.
Environmental Science: Environmental time series data can help us explain the rise in temperature over the past few years. Plot shows the temperature data over a period of time
Time series data collected over different points in time breach the assumption of the conventional statistical model as correlation exists between the adjacent data points. This characteristic of the time series data breaches is one of the major assumptions that the adjacent data points are independent and identically distributed. This gives rise to the need of a systematic approach to study the time series data which can help us answer the statistical and mathematical questions that come into the picture due to the time correlation that exists.
Time series analysis holds a wide range of applications is it statistics, economics, geography, bioinformatics, neuroscience. The common link between all of them is to come up with a sophisticated technique that can be used to model data over a given period of time where the neighboring information is dependent.
In time series, Time is the independent variable and the goal is forecasting.
Who this course is for
Students, Quantitative and Econometrics Modellers, Financial markets professionals

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