Time Series Modeling in Excel, R, and Power BI
2hAdvanced2023-03-23
Authors
Helen Wall
Data analytics and business analysis expert
Course details
The use of time series models has become a central topic in today’s data science world. In this course, instructor Helen Wall shows you how to run autoregressive integrated moving average (ARIMA) models as predictive, time series modeling tools in Excel, R, and Power BI.
Explore the building blocks of a time series decomposition, which lets you make data forecasts with accuracy, consistency, and ease. Take a deep dive into the fundamentals of autoregressive coefficients, autocorrelation, moving average coefficients, stationarity and random walks with integrated components, and how to forecast time series models using Power BI. By the end of this course, you’ll be ready to start wielding your new data analytics skills to make more timely and effective decisions for your business.
Explore the building blocks of a time series decomposition, which lets you make data forecasts with accuracy, consistency, and ease. Take a deep dive into the fundamentals of autoregressive coefficients, autocorrelation, moving average coefficients, stationarity and random walks with integrated components, and how to forecast time series models using Power BI. By the end of this course, you’ll be ready to start wielding your new data analytics skills to make more timely and effective decisions for your business.
Skills covered
RPower BIStatisticsBusiness AnalyticsBusiness IntelligenceMachine LearningSpreadsheetsMicrosoft ExcelData AnalysisArtificial Intelligence (AI)Programming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceMicrosoftSoftware DevelopmentDeep Dive (X:Y)
Concepts
0. Introduction
- 01 - Predicting the future with time series models
- 02 - What you should know
- 03 - Course project
1. Time Series Models
- 04 - Time series models
- 05 - Dates
- 06 - Date time index
- 07 - Time series objects
- 08 - Filtering
- 09 - Removing NAs
- 10 - Aggregating and grouping
- 11 - Time series decomposition
- 12 - Adding fitted lines
- 13 - Forecasting future trends
- 14 - Challenge - Aggregating time series data
- 15 - Solution - Aggregating time series data
2. Autoregression
- 16 - Linear regression best fit lines
- 17 - Residuals
- 18 - Lags
- 19 - Autoregression
- 20 - Moving average
- 21 - Challenge - Determining overall trends
- 22 - Solution - Determining overall trends
3. Time Trends
- 23 - Autocorrelation
- 24 - Partial autocorrelation
- 25 - Stationarity
- 26 - ARIMA modeling
- 27 - Power BI modeling
- 28 - Challenge - Forecasting next week's demand
- 29 - Solution - Forecasting next week's demand
Conclusion
- 30 - Extending your time series knowledge