What is Time Series Forecasting in Machine Learning?
Time Series is a certain sequence of data observations that a system collects within specific periods of time — e.g., daily, monthly, or yearly. The specialized models are used to analyze the collected time-series data — describe and interpret them, as well as make certain assumptions based on shifts and odds in the collection. These shifts and odds may include the switch of trends, seasonal spikes in demand, certain repetitive changes or non-systematic shifts in usual patterns, etc.
All the previously, recently, and currently collected data is used as input for time series forecasting where future trends, seasonal changes, irregularities, and such are elaborated based on complex math-driven algorithms. And with machine learning, time series forecasting becomes faster, more precise, and more efficient in the long run. ML has proven to help better process both structured and unstructured data flows, swiftly capturing accurate patterns within massifs of data.
Market :
According to Verified Market Research, the Global Time Series Databases Software Market was valued at USD 273.56 Million in 2020 and is projected to reach USD 575.03 Million by 2028, growing at a CAGR of 10.06% from 2021 to 2028.
Applications of Machine Learning Time Series Forecasting
Pretty much any company or organization dealing with constantly generated data and the need to adapt to operational shifts and changes can use time series forecasting. Machine learning serves as the ultimate booster here, allowing to better handle:- Stock prices forecasting
- Demand and sales forecasting
- Web traffic forecasting
- Climate and weather prediction
- Demographic and economic forecasting
- Scientific studies forecasting
Time Series Forecasting Machine Learning Methods
Legacy Methods of Time-Series Forecasting:- Recurrent Neural Network (RNN)
- Long Short-Term Memory (LSTM)
Classic Methods of Time-Series Forecasting:
- Multi-Layer Perceptron (MLP)
- ARIMA
- Bayesian Neural Network (BNN)
- Radial Basis Functions Neural Network (RBFNN)
- Kernel regression or Generalized
- Regression Neural Network (GRNN)
- K-Nearest Neighbor Regression Neural
- Network (KNN)
- CART Regression Trees (CART)
- Support Vector Regression (SVR)
- Gaussian Processes (GP)
Topical Methods of Time Series Forecasting:
- Convolutional Neural Network (CNN)
- Attention Mechanism
- Transformer Neural Networks
- LightGBM
- Decision Trees
- XGBoost
- AdaBoost
Key Challenges of Forecasting Time Series with Machine Learning Models
- Lack of Time-Related Data
- Acceptable Accuracy for Model
- Evaluation
- Lack of Understanding of Domain
- Business Processes