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Time Series Analysis & Its Applications

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  • 0:04 Predicting the Future
  • 0:31 Time Series Analysis
  • 2:29 Examples
  • 4:54 Lesson Summary
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Lesson Transcript
Instructor: James Blackburn

James has an MBA from Auburn University and a MA in Humanities from Cal State-Dominguez Hills He writes on leadership, business strategy and finance.

In this lesson, we will explain time series analysis, its purpose and uses. We will define trend, cyclical and seasonal variances. We will solve a real-world business problem using time series analysis.

Predicting the Future

NRAR Robotics recently introduced a new line of personal robots to the consumer market. After about three months of slow sales, the sales of robots had increased to an average of 10% for the next nine months. It takes the company about three months to build a new robot. The management team needs to forecast the demand for three months in advance to ensure enough robots are available. Time series analysis helps the team improve the forecast.

Time Series Analysis

Time series analysis is the collection of data at specific intervals over a period of time, with the purpose of identifying trends, cycles, and seasonal variances to aid in the forecasting of a future event. Data is any observed outcome that's measurable. Unlike in statistical sampling, in time series analysis, data must be measured over time at consistent intervals to identify patterns that form trends, cycles, and seasonal variances. Measurements at random intervals lose the ability to predict future events.

Trends are consecutive increases or decreases in a measurement over time. A trend could last several days, months, or years. In almost every observation, a trend will reverse itself during the lifetime of measurement. This reversal is sometimes referred to as a correction. Corrections occur in the economy, in a stock market, and in a business. It normally follows unprecedented growth or loss. A cycle, on the other hand, is a pattern of growth, followed by a decline, followed by growth. The cycle is recognized by the pattern of a rise and fall repeated over several periods of measurement.

Seasonal variances are measured over several months and are associated with a specific time of the year. Retailers realize a seasonal growth in sales during the months of November and December. For the rest of the year, sales are relatively flat. The year can be divided into four quarters. The first three quarters show a small volume of sales giving way to a large growth in sales in the fourth quarter.

The insights discovered during a time series analysis, such as the spike in sales for the retailer, can be used to forecast demand for subsequent business periods. In fact, many retailers place purchase orders for the holiday shopping season months or even a year in advance based on these forecasts.

It's important to remember that in any analysis of trends, past performance does not guarantee future performance. Just because a customer purchased 200 units of material last year, doesn't mean that they still have the same need this year.

Examples

Now that we've reviewed the basic concepts of time series analysis, let's return to the problem faced by the management team: how many robots should be started? We start with a quick examination of the sales data from the last 14 months.

2016 Robots Sold 2016 Robots Sold 2017 Robots Sold
Jan 45 July 133 Jan 200
Feb 35 Aug 146 Feb 220
Mar 60 Sep 161
Apr 100 Oct 177
May 110 Nov 195
Jun 121 Dec 235

As we observe from the sales data, the robot sales in February had a slight decline from the previous month. Between March and April, a big increase in sales was realized. The percent increase can be measured by subtracting the sales from February from the March, then dividing the result by the sales in February.

NRAR Percent of Change

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