In the digital age, time-series data forecasting has transformed from a theoretical research into a key tool in the actual operation of various industries. This kind of forecasting can cover a wide range of business areas, such as: using historical sales data to predict future sales trends based on past
power
consumption Predict future power demand
based on data Predict future stock price trends based on past stock market conditions
...
Time series data forecasting can provide powerful strategic support for enterprises, help achieve more accurate resource allocation, improve operating efficiency, and improve understanding of future changes Its predictability helps companies better deal with uncertainties, so more and more companies start to use this method to promote their own digitalization process. However, in actual operation, the following three common challenges are often faced:
Picture
▶ Data preprocessing workload is heavy: time series data often contains a lot of noise, missing values and outliers, and cleaning and preprocessing takes a lot of time and effort. energy. At the same time, how to deal with complex characteristics such as seasonality and trend, and how to decompose and integrate the original sequence data into trends requires professional technology and experience.
▶ High degree of professionalism in model construction: Traditional statistical methods work well when dealing with simple linear problems, but are weak when dealing with complex and nonlinear time series data prediction problems. Although advanced algorithms such as deep learning can provide higher prediction accuracy, they require professional programming and algorithm knowledge, which limits the use of most users.
▶ Scheduling and deployment is difficult: After the algorithm model is constructed, how to deploy it to the actual business and carry out effective scheduling management often requires complicated code work and requires high system stability and performance.
How to solve these problems efficiently and intelligently? The "Integrated Solution for Time Series Data Forecasting" created by Merrill Lynch Data may be able to help you!
The integrated solution for time-series data prediction
In the process of time-series data prediction, it involves the whole process from data preprocessing, model building to scheduling and deployment. cooperation between departments.
The solution proposed by Merrill Lynch Data, with the support of Tempo intelligent tools, makes the preparation of each link more efficient. For business personnel, only three simple steps are needed to complete the time series data prediction.
The first step, data preprocessing
Through the powerful data preprocessing capabilities of the Tempo machine learning platform, users only need to drag and drop to complete data preprocessing such as cleaning, filling, smoothing, and sampling.
The second step, model building
Tempo machine learning platform has built-in rich time series algorithms, including ARIMA, sparse time series, X11, gray prediction, echo state network, etc., supports automatic feature selection and parameter tuning, making model building easier For simplicity and efficiency.
Step 3: Scheduling deployment
After the model is constructed, Tempo scheduling can help users implement model deployment and scheduling management, support multiple deployment methods, and flexibly adjust the scheduling frequency and sequence to meet the needs of different businesses.
At present, the "integrated solution for time-series data forecasting" has been applied to the fields of gas demand forecasting, power grid electricity sales forecasting, shearer drum height forecasting, distributed photovoltaic output forecasting, fan energy efficiency forecasting research and other fields. After a lot of practice and experience accumulation , the accuracy rate is stable at more than 90%, gradually realizing the substitution of machines, and empowering traditional manufacturing enterprises to realize the reform and upgrading of intelligent manufacturing.
Small T's summary
From efficient logistics and supply chain systems, to investment decisions in the financial market, to demand forecasts in the energy sector, high-precision time-series data forecasts play a vital role in all walks of life.
Whether you are a business person who does not understand code, a professional data scientist, or an enterprise decision maker, Merrill Lynch's "Time Series Data Forecasting Integrated Solution" can help you easily meet the challenges of time series data forecasting and realize data-driven decision - making , enabling enterprises to reduce costs and increase efficiency, thereby promoting the digitalization process of enterprises.