Whenever data is recorded at regular time intervals, it is called time-series data. Simply put, time-series data is data that is collected at different points in time - as opposed to cross-sectional data, which focuses on specific points of time.
For example, weather records, population, birth rates - are examples of time series data. Other examples are Gross Domestic Product (GDP), Consumer Price Index (CPI), S&P 500 Index, and unemployment rates, which are data used in the field of Economics.
There is a wide range of frequently used time series forecasting methods.
Time series forecasting is looking at recorded data over time to forecast or predict what might happen in the next time period, under the assumption that future trends will be similar to historical trends.
It’s a predictive analytics technique used across many fields to predict events based on patterns and historical data - and has been a staple of data analysis for most of the modern era.
What is Time Series Forecasting used for?
In the field of eCommerce, time series forecasting is used to predict consumer demand, inform inventory management and warehousing, as well as anticipate future sales, revenues, and possible fluctuations.
Time series forecasting is also used in various fields for many purposes. Some of these applications are:
There are two main classifications of forecasting methods:
Quantitative Forecasting Methods - Quantitative forecasting uses historical data from time-series or correlation information. There are four different approaches you can use:
Naive Approach - Entails looking at previous trends and assuming that these will hold true for the next time period
Trend Projection - Involves looking at the trajectory of what is happening in terms of increases or decreases and using that to forecast what will happen in the next time period
Moving Averages - Getting the average of a range of past periods (Ex. past three periods’ average) and assuming the average will be the ballpark estimate for the next time period; ‘Moving’ these averages as periods progress
Exponential Smoothing - Taking a weighted average approach at moving averages; Attributing weight to more relevant periods differently, depending on the forecast
Qualitative Forecasting Methods - Qualitative forecasting takes into account opinions from experts, decision-makers, or customers. There are also four different approaches you can use:
Delphi Method - Enlisting the opinion of trusted advisors and consumers regarding what is going to happen based on their consumer behavior, compiling this data, and presenting it to decision-makers.
Executive Opinion - Looking to a group of executives to decide on what is going to happen in the next period.
Consumer Surveys - Asking consumers if they would purchase something and taking this data to project consumer demand.
Sales Force Estimates - Asking individual salespeople to make their own forecasting estimates and taking that data to forecast what is going to happen in the next period.
2. Understanding the Limits of Your Historical Data
As with any approach, time series forecasting also has its weaknesses. Generally, one of the most common issues with historical data is the probability of having outdated and/or inaccurate data.
For example, this may affect data integration in cases where an item you sold is no longer in stock or has been deleted from your system. Other related problems are issues of generalization, limited data, and studies, as well as problems with accurately identifying the correct data representation model.
How Does Time Series Forecasting Help?
Time series forecasting uses time series metrics to help inform business decisions and plan future developments in terms of projected revenue, sales, demand for resources, and product deliverables.
A lot of organizations and businesses have used time series forecasting to maximize sales and even predict crypto market performance. This is because accurate forecasts are highly valuable in corporate planning.
Insights from this kind of analysis can allow an organization to efficiently allocate its resources, budget its funds, and make smart business decisions towards growth.
Time series forecasting allows you to do revenue forecasts that allow you to take into account what has worked for you in the past, where you continue to drive business, and how you can plan for better financial security down the road. Taking into account your business revenue helps you make strong business decisions based on concrete information (your past performance) and actionable insights (current trends in light of projections).
Time series forecasting can help businesses plan and develop business strategies. It helps businesses understand cause-and-effect in a way that’s backed by historical data. Since it is largely dependent on analyzing the past to forecast the future, it can help businesses find out what works for them and what doesn’t, and thereby come up with a robust business plan for the next period.
It is widely used in financial and business organizations for decision-making and policy planning.
3. Demand Planning to Prepare Item Stock Levels
Anticipating demand is one of the biggest advantages of time series forecasting. At the core of any business is the need to attract more sales, and therefore knowing the forecast regarding market demand will help businesses remain agile in anticipating customer needs.
Planning for customer demand is a very efficient way for businesses to effectively prepare item stock levels according to forecasted values. With the correct stock levels, businesses can avoid surplus and maximize revenue.
This can also be helpful for businesses that might want to branch out by introducing new products or targeting new market segments. With proper demand planning, they can prepare appropriate stocks based on the projected market demand.
Some Time Series Forecasting Models
There are various time series forecasting models you can use:
1. Exponential Smoothing
Forecasts produced using this method are weighted averages of past observations. These weights decrease exponentially over time, meaning, more recent observations have a higher weight. Generally, this model yields reliable forecasts for a wide range of time.
2. Dynamic Linear Models
Dynamic linear models (DLMs) treat parameters as time-varying rather than static. Commonly used in econometrics, DLM may include other relevant information on top of historical data (Ex. effect of holidays, economic changes, new laws, competitors, etc.). This model may lead to more accurate forecasts in instances where historical variation needs to be taken into account.
Prophet is an open-source software by Facebook’s Core Data Science team and is a procedure for ‘forecasting time series data based on an additive model.’ Non-linear trends are fit with yearly, weekly, and daily seasonality plus effects of holidays. According to them, it works best with time series that are strongly affected by seasonal trends.
4. ARIMA, SARIMA
ARIMA (Auto Regressive Integrated Moving Average Model) is used for non-stationary data that has a trend component. It is widely used and usually complementary to exponential smoothing. However, although ARIMA can handle data with a trend, it is not able to support time series with a seasonal component.
That’s where SARIMA, an extension to ARIMA, comes in. It supports the direct modeling of seasonal components. It stands for Seasonal Autoregressive Integrated Moving Average.
Time series forecasting can be very valuable to e-commerce businesses that want to maximize their revenue. By looking at historical data and acknowledging trends and strategies that have worked for them in the past in light of current trends, they can come up with a solid business plan and optimize business decisions to further business growth.
When done manually, gathering data and developing complex machine learning algorithms can be a tremendously tedious task.
Fortunately, with the use of no-code machine learning tools like Graphite, a company can maximize historical data to create a machine learning time series forecasting model that generates accurate results. The best part is that since Graphite is a no-code machine learning platform, teams can train, build and deploy models without writing a single line of code!
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