# Introduction to Simple Forecasting Methods

**Forecasting**is one of the most important aspects in business. We use the techniques to anticipate the future.

**manpower, budget**etc. Forecasting is so powerful that all the business planning relies on forecasting.

**WFM**bucket and due to this, WFM is looked as a very important and pivotal department in an organisation.

**calculations**and usage.

**Forecast Accuracy/Error**.

**As always, I have attached a excel sheet at the end which contains detailed calculations of the Simple Forecasting Methods.**

**So, let’s begin our quest!!**

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__Naive Forecasting Method or Random Walk Method__

__Naive Forecasting Method or Random Walk Method__**Naive Forecasting Method**is that the forecasted values will be equal to the previous period value. The Naive Method is also called as

**Random Walk**Method.

**forecasted**value will be equal to December. This is illustrated by a formula as shown below.

**previous**period.

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__Seasonal Naive Forecasting Method or Seasonal Random Walk Method__

__Seasonal Naive Forecasting Method or Seasonal Random Walk Method__**seasonality**. While the forecasters were using the Naive Method, they had a problem of the previous seasonality not getting factored in the forecast.

**Seasonal Naive Method**was formulated.

**previous seasonal**period.

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__Random Walk with Drift__

__Random Walk with Drift__**Seasonal Random Walk**, but with an additional component called drift.

**Level, Trend and Seasonality**. These are called the “Building blocks of Forecasting”. I would urge the reader to clink on this link to read more on the Building blocks of Forecasting.

**Contact Center Basic Forecasting Building Blocks**

**seasonality**part of the forecast is taken care of, the actual historical value acts as a

**Level**.

**Trend**. To factor this in our forecast, we use the additional

**drift**component to our forecasting.

**Random walk with Drift**.

**Please refer to the attachment.**

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__Moving Average Method__

__Moving Average Method__**average of subset**of previous data as the forecasted value.

**Moving Average**.

**MA(n)**and n stands for the period of the subset. We read it as

**Moving Average of Order n**.

**monthly call volume data**for a year and we are trying to forecast the value for the next 4 months, we can use the average of the first four months of previous data for Month 1. When we move to Month 2, the average also moves to the next four month

**excluding**the 1st month and including the 5th month. Thus the average move as we progress to our forecast.

**MA(4).**

**trail and error**method.

**Moving Average**.

__Weighted Moving Average Method__

**equal importance**to all the values in the subset. However there are instances where the most recent historical value has some external factor to it such as Marketing, New Product etc.

**Weighted Moving Average Method**.

**WMA(n)**and n stands for the period of the subset. We read it as

**Weighted**

**Moving Average of Order n**.

**historical value**gets more weightage and the weightage reduces as we go further to the past. However all the weights added together should be equal to 1 or 100%.

**WMA(4).**

**trail and error**method.

__How to find the best fit model?__

*how do we decide which model is the best fit for our data?*

**forecast accuracy.**

**The difference between the forecasted value and the actual value.**

__Error:____Absolute Error:__The absolute difference between the forecasted value and the actual value.

**The square of the Absolute Error.**

__Square of Error:__**forecasting error**.

: Average of the Absolute error for all the time period.*MAE (Mean Absolute Error)*: Square Root of the Average of the Squared Error for all time period.*RMSE (Root Mean Square Error)*: Average of Absolute Error divided by the average of actual value for all the time period*MAPE (Mean Absolute Percentage Error)*

**MAE, RMSE and MAPE**.

**Seasonal Naive Method or Random Seasonal Naive**is the best fit model for forecasting.

### Attachment: Simple Forecasting Model

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