• Time series analysis is a statistical method used in market research to analyze and forecast sales data. It involves studying the pattern and trend of sales data over time to make predictions about future sales.

#### Methods:

There are various quantitative methods used in time series analysis, some of which are:

1. Moving average: This method involves calculating the average of a fixed number of periods, such as months or quarters, and using it to forecast future sales. For example, if the average sales for the last 6 months were \$50,000, then the forecast for the next 6 months could be \$50,000.
2. Exponential smoothing: This method involves weighting the most recent sales data more heavily than older data to give more importance to recent trends. For example, if the sales for the last 3 months were \$30,000, \$35,000, and \$40,000, the forecast for the next month could be calculated using exponential smoothing with a weight of 0.3 for the most recent month: forecast = 0.3(\$40,000) + 0.7(average of last 2 months) = \$37,000.
3. Trend analysis: This method involves analyzing the trend in sales data over time and using it to make predictions. For example, if sales have been increasing steadily by 5% each year for the past 5 years, then the forecast for next year’s sales could be calculated by increasing this year’s sales by 5%.

1. Time series analysis is a reliable method of sales forecasting as it is based on historical sales data.
2. It allows businesses to identify trends and patterns in sales data that can inform decision making.
3. It helps businesses to plan production, inventory and staffing levels in advance.
4. It can be automated using software, which saves time and reduces the risk of human error.
5. It can be used to forecast sales for different time periods, such as days, weeks, months, and years.
6. It provides a quantitative basis for decision making.