Time series forecasting is an area of machine learning that is often overlooked. Since time components are such an important variable in much of predictive analytics, it is essential to consider the practical business use cases for time series predictions. Time series analysis can help you extract meaningful statistics, including identifying patterns, trends, relevant gaps, and any outliers within your data over time. While time series analysis is meant to describe and understand data, time series forecasting is when this analysis is used in a model to make reasonable predictions about the future to make informed business decisions.
There is an endless list of use cases for time series forecasts where practical business insight can be derived within any industry, including healthcare, finance, retail, manufacturing, transportation, etc. Here are just a few examples:
- Daily gas prices that impact the cost of transporting or delivering goods
- Investing in the stock market or cryptocurrency, short and long term
- Product sales and units expected to be sold daily, monthly, and/or yearly
- Monthly inventory requirements and control to have the correct inventory to meet demand and minimize the cost of holding inventory
- Predicting equipment failures and maintenance requirements to minimize downtime and uphold safety standards
- Predicting near and long-term market pricing to set prices of goods and services offered or to gain insight into costs
- Forecasting real estate market behaviors and trends
- Understanding and anticipating transportation patterns of car traffic, train and bus delays, and air traffic patterns
But before we can make use of time series predictions, it is important to understand the nature of this type of analysis.
Understanding the components
There are four main components of the behavior of time series data that allow for effective analysis and accurate prediction to be made. Accounting for these components is a powerful way to clean, organize, analyze, and ultimately make the most accurate predictions from your data.
- Trend – overall long-term direction of the series.
- Seasonality – repeated behavior in data that occurs at regular intervals.
- Cycle – an up and down pattern that is not seasonal.
- Unexplained variation – irregularities such as a one-off event.
With these components, dozens of algorithms and various techniques have evolved over the years. Common models include AR, MA, ARMA, and ARIMA, which can be used in an analysis to choose the most relevant data within a dataset to produce the best possible forecast/prediction. For a more in-depth explanation of these data science methods, please refer to A Comprehensive Guide to Time Series Analysis written by Shanthababu Pandian and published by Analytics Vidhya.
The current and future state of time series predictions
Time series forecasts have been around for some time, going back to the 1920s. Of course, over the decades, the approach, process, availability of data, and technological tools have changed considerably. As we move into the future, time series predictions will become increasingly automated. Today, we have advanced tools, such as machine learning (ML), which have become more accessible to everyday users without advanced knowledge of data science in the form of Auto(ML). There is also an explosion of data, available in various types and magnitudes never before seen, which is expected to continue and further build the case for time series predictions.
Steps to generate forecasts
- Define the specific purpose of your forecast.
- Identify the relevant data points to be used.
- Gather data to be input into the model.
- Determine the relevant time parameters.
- Select the forecast model type.
- Make the forecast.
- Check and apply the results.
As mentioned previously, many of these steps can be automated or aided with Auto(ML). Auto(ML) can help identify relevant data points, load and cleanse data, and determine relevant time parameters. Further, Auto(ML) automates model and algorithm selection while forecasting and testing the model.
While time series forecasting has historically been underutilized, there has been a noticeable shift in recent years. With its practical and powerful data science use cases that generate forward-looking insights to make meaningful business decisions, its utility and strategic value are now readily accessible to the everyday user through Auto(ML).
Are you looking to power up your decision-making with advanced time series forecasting? Drop us a note at email@example.com. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.