9 see pic **Title: Appropriate Forecasting Techniques for Time-Series Data with an Increasing Trend****Introduction:**In analyzing time-series data, identifying patterns such as trends and seasonality is crucial for selecting the most suitable forecasting techniques. When data demonstrate an increasing trend without noticeable seasonality, specific methods can be more effective.**Problem Statement:**Suppose a time-series plot shows that the data in question exhibit an increasing trend. The plot shows no evidence of seasonality. Which of the following forecasting techniques would be appropriate?**Options:**1. Moving average or simple exponential smoothing would both be appropriate2. Moving average3. Simple exponential smoothing4. Double exponential smoothing**Discussion:**The main challenge is to select a technique that can accommodate the upward trend without introducing errors from nonexistent seasonal patterns. While both the moving average and simple exponential smoothing can smooth data, double exponential smoothing is specifically designed to handle trends, making it a potentially better choice for data exhibiting a clear upward trajectory.

Answered: no evidence of seasonality.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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**Title: Appropriate Forecasting Techniques for Time-Series Data with an Increasing Trend**

**Introduction:**
In analyzing time-series data, identifying patterns such as trends and seasonality is crucial for selecting the most suitable forecasting techniques. When data demonstrate an increasing trend without noticeable seasonality, specific methods can be more effective.

**Problem Statement:**
Suppose a time-series plot shows that the data in question exhibit an increasing trend. The plot shows no evidence of seasonality. Which of the following forecasting techniques would be appropriate?

**Options:**

1. Moving average or simple exponential smoothing would both be appropriate
2. Moving average
3. Simple exponential smoothing
4. Double exponential smoothing

**Discussion:**
The main challenge is to select a technique that can accommodate the upward trend without introducing errors from nonexistent seasonal patterns. While both the moving average and simple exponential smoothing can smooth data, double exponential smoothing is specifically designed to handle trends, making it a potentially better choice for data exhibiting a clear upward trajectory.

Expert Solution

Step 1

Since, the provided information of the problem suggest that time series data contains increasing trend without seasonality. So, we need to add an extra term to take into account the possibility of trend. The technique of time series forecasting which includes these conditions is double exponential smoothing.

In time series data, moving average is used to determine whether there is upward or downward trend.

Simple exponential is used to forecast the time series data when it can be described by a additive model with constant level and without seasonality.

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