Time series forecasting has always been of high importance for businesses for ever. It lays the foundations for sales forecasting, financial forecasting and all other business forecasting variables. Time series is a set of observations on a quantitative variable collected over time. Businesses collect various data to aid in financial forecasting, sales forecasting, technical analysis, business inventory forecasting, etc.

 

Forecasting is the essence of business. Profits are made only with the right forecasts. All managers use forecasts to decide how much inventory to buy, number of people to hire, annual advertisement spend, etc. Time series forecasting is one of the keys to forecasting systematically. A number of time series forecasting models have been developed to aid businesses in make the right forecasts and improve its profits.

 

Graduate Tutor's Statistics Tutor Group is well equipped to tutor you in the various Time Series Forecasting methods and techniques. Work with our tutors online and learn the concepts underlying the Time Series Forecasting methods. Apply the Time Series Forecasting methods on sample data from a wide variety of industries to consolidate your reinforce techniques. Use commonly available time series forecasting tools including microsoft excel or crystal ball simulation software. See a sample of the topics, forecasting methods and tools you can learn with Graduate Tutor's Statistics Tutor Group below. Please email us if you do not find what you are looking for below and we will be happy to assist you in finding what you need.

 

Time Series Forecasting Methods
Measuring Accuracy of time series forecasting methods
Combining Forecasts
Stationary Models for Business Forecasts  
Moving Averages for Business Forecasts
Time Series Forecasting with the Moving Average Model 
Weighted Moving Averages for Sales Forecasts  
Forecasting with the Weighted Moving Average Model 
Exponential Smoothing 
Forecasting with the Exponential Smoothing Model 
Time Series Forecasting with Seasonality effects  
Time Series Forecasting Model for Stationary Data with Additive Seasonal Effects 
Time Series Forecasting Model with Stationary Data with Multiplicative Seasonal Effects 
Time Series Forecasting Models for data with Trend
Double Moving Average Time Series Forecasting Models
Double Exponential Smoothing {Holt's Method) 
Time Series Forecasting with Holt's Method 
Time Series Forecasting with Holt-Winter's Method for Additive Seasonal Effects 
Time Series Forecasting with Holt-Winter's Additive Method 
Holt-Winter's Method for Multiplicative Seasonal Effects 
Forecasting with Holt-Winter's Multiplicative Method 
Modeling Time Series Trends Using Regression 
Time Series Forecasting with the Linear Trend Model 
Forecasting with the Quadratic Trend Model 
Time Series Forecasting with the Quadratic Trend Model 
Modeling Seasonality with Regression Models 
Adjusting Trend Predictions with Seasonal Indices 
Computing Seasonal Indices 
Forecasting with Seasonal Indices
 Refining the Seasonal Indices 
Seasonal Regression Models 
The Seasonal Model 
Time Series Forecasting with the Seasonal Regression Model 
Time Series Forecasting using the Crystal Ball Predictor 
Using CB Predictor 

 

Other operations research and statistics topics that Graduate Tutor's Statistics Tutor Group tutor include:

 

Simple Linear Regression

Multiple Linear Regression

Discriminant Analysis

Simulation using Crystal Ball Software

Spreadsheet Modeling

Financial Modeling using Microsoft Excel