Time Series Analysis

What is Time Series Analysis?

Time is an essential factor to any level of business operations. Businesses have, from the beginning, relied on historical data to make assumptions on their day to day operations, be it purchase decisions, inventory management or staffing. Time series analysis, simply put, is a statistical technique that deals with time-series data. Time series data refers to a series of data points ordered by time.

Forecasting using time series analysis [1]

Time series analysis is often used to clarify and predict causality behaviours using past data. It can be used to see how assets and other economic variables change over time or to examine the changes in one variable compared to other variables over the same period. Time series analysis is used extensively in the finance sector but has various use cases in the science, engineering and health sectors. Time series analysis often outperforms other simpler methods for forecasting [2]. Election forecasting, budget forecasting, trading, and weather predictions are some areas of interest.

Trend, seasonality, and randomness from an observed time-series data [3]

Why is it used?

Most of a business’s data is time-series data. The intervals of data collection can range from seconds to years. An essential factor of the time series analysis method is capturing underlying structures common to time series data, such as trend and seasonality [4]. In addition, time series analysis, compared to other simpler methods, accounts for real-world conditions, resulting in higher forecasting accuracy. 

A business can use time series analysis to reduce overall costs in business, given good data. Time series analysis can be vital in economic forecasting. A business can make predictions about the market and the economy in order to direct its activities. Sales forecasting can be done using historical business data, thereby assisting in inventory management. Power companies, for example, can forecast the usage of power by their customers and provide customised plans for certain times of the day or depending on the climate. Industrial data can be used for yield projections, workload projections, quality control and much more. The business can plan its operations for special events like public holidays or offer periods where there is a change from the expected level of sales or services. 

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