Time Series Forecasting

Time is the fundamental essence of our lives, as everything happens within time. In the business world, time series forecasting is a crucial task that help companies make important decisions. Forecasting is a challenging task because of complex patterns and uncertainties.

A time series comprises elements such as:

Trend: The long-term movement or directionality, showing whether it's increasing, decreasing, or staying relatively constant over time.

Seasonality: Regular patterns that occur at consistent intervals within the time series, often linked to calendar periods like days, weeks, months, or seasons.

Cyclical Variation: Repeating up-and-down movements that are not of fixed frequency like seasonality, often associated with economic cycles or other non-seasonal fluctuations.

Irregularity/Randomness: Unpredictable fluctuations or noise in the data that do not follow a specific trend, seasonality, or cycle. These irregularities can be caused by random events or unforeseen factors.

There are various types of time series problems, such as demand forecasting, financial forecasting, healthcare forecasting, traffic prediction, and human action prediction. Each problem has its unique solution space, and SmartOpt® doesn't rely on fixed tools to address these diverse problems. We follow essential steps when developing a forecasting solution:

•    Define the problem.

•    Conduct Exploratory Data Analysis.

•    Perform a literature review to find state-of-the-art methods that are best-fit for the problem.

•    Use these methods as benchmarks and strive to enhance them.

•    Evaluate models using several metrics (MAE, RMSE, MAPE, sMAPE, Relative MAE) with extensive back-tests.

•    Implement live tests and refine the models for improvement.

 

The diversity of the fields we work in accumulates into a pool of models, enabling us to specialize in time series. Leveraging various methods such as machine learning, deep learning, and statistical/mathematical modeling, we ensure the most suitable approach to the problem. We select the most effective models with robust back-testing mechanisms, striving to achieve highly successful predictions as much as possible. We dedicate considerable time to each project, setting us apart from other companies...