The ability to build predictability in business models provides businesses incredible differentiator. Time series forecasting using machine learning, which is an evolutionary model that is contributing in a small way to making reliable predictions, is realizing this distant possibility slowly.
Every day, many useful tools are being launched in the market to help us make vital predictions and find opportunities in fields like stock markets, agriculture, retail, banking, medicine and healthcare, meteorology and more.
What is a Time Series?
Time series is the sequence of measurements done over time, usually obtained periodically (daily, monthly, quarterly, or yearly) over intervals that are equally spaced. Time series analysis comprises of methods that help extract meaningful statistics and other data characteristics with a structured model to predict future values based on historical data recorded at specific times.
Time series are used extensively for dynamic data analysis in fields like economics, weather predictions, stock markets, control engineering, signal processing, astronomy, and retail sales.
What is Forecasting?
Forecasting is the process of making predictions taking past and present data into account, along with the analysis of trends that influence them. Forecasting takes models that fit on historical data and uses them to predict future observations.
Time series forecasting starts with a historical time series. Analysts examine the historical data while checking for patterns of time decomposition, such as trends, seasonal patterns, cyclic patterns, and regularity.
What is Machine Learning?
Machine learning is the application of Artificial Intelligence (AI) that helps machines learn and improve autonomously from experience without being explicitly programmed.
The process of cognitive learning begins with observations or data and mapping them to cognitive conception concepts like examples, direct experience, alternative action, or instruction, to look for patterns in data, infer the consequences and make better decisions in the future or adjustments accordingly.
Using Machine Learning for Time Series Forecasting
Managed Services like Amazon Forecast combine time series data with additional variables to build forecasts using machine learning. The complicated relationship between demand and the dynamic factors guiding it is hard to determine naturally, but machine learning ideally recognizes it.
Amazon Forecast will automatically examine the data provided, identify the significant parts, and produce a forecasting model capable of making accurate predictions (up to 50%) than considering time series data alone.
Forecasting helps make predictions on time-series data to estimate:
- Operational metrics, such as web traffic routing to servers, service usage, or IoT sensor metrics.
- Business metrics, such as sales, profits, and expenses.
- Resource requirements, such as the needed bandwidth.
- The volume of inputs required by a manufacturing process like raw materials, services, etc.
- Retail demand considering the impact of price discounts, marketing promotions, and other campaigns or challenges.
Let us examine some of the interesting use cases that delineate how Time Series Forecasting and machine learning have led to pivotal transformations in this blog.
Time Series Forecasting and Machine Learning are being utilized extensively in areas where periodic datasets can be leveraged by following dynamic data processing patterns and models.
Product Demand Planning
Predicting the inventory levels for each distinctive store can be done effectively with time series forecasting using machine learning by a retailer handling multiple store locations. Today, many top-notch services are available in the market that can be incorporated into your retail management software.
Services like Amazon Forecast have a smooth process where information relevant to forecast like pricing, store promotions, store locations, historical sales, and catalogue data from your retail management systems need to be collected.
Support data like website traffic logs, weather, and shipping schedules can then be combined to accurately forecast customer demand for products (based on segmentation) at the individual store level.
The pricing structure (MRP, discounts, taxes) can also be determined by the machine learning algorithm, weekday and weekend historical sales data, returns/exchanges, customer preference for a particular type of product based on its attribute (color, size, material), and seasonal aspects/purpose are also vital system inputs.
Predicting resource requirements (human and non-human) over time is pivotal for every business. It is based on forecasts around crucial information like expenses, revenue, and cash flow. Cost control through the accurate estimation of a business’s needs is essential to save up on costs.
Using services like Amazon Forecast, organizations can retrain the model on a scheduled basis for volatile datasets to keep predictions updated. Better Amazon Forecast predictions IT capacity, logistics, web traffic, manufacturing, travel demand, and the details of the next batch of order volumes based on that data. This integration could help predict demand for third-party resources and their size.
Resource availability, revenues, and costs can be optimized by importing historical data based on categories, geographic regions, local customer preference, content metadata, and other parameters.
Sensor-generated weather data offers input for time series forecasting in meteorological surveys and forecasts using the machine learning paradigm.
Precise information on the upcoming weather conditions can help plan manufacturing processes or sales strategies precisely. Machine learning for time series forecasting, large quantities of meteorological data can be imported from multiple sources, including satellites and global and historical weather data providers.
Cloud service and services like Amazon Forecast meets these needs.
Managed services like Amazon Forecast offer the combined ability of time series forecasting and advanced machine-learning algorithms to products without having to build and train models manually. This makes the customer’s products simple to develop and maintain while leveraging accuracy, availability, and scalability through exploratory data analysis.