Measuring change in data is vital for understanding the world, and tools like PostgreSQL and TimescaleDB help us analyze historical data to predict future trends and values. Time-series forecasting is a technique that uses historical and current data to make informed decisions about business strategy and understand future trends. This method can be used in various industries such as business planning, control engineering, cryptocurrency trends, financial markets, modeling disease spreading, pattern recognition, resources allocation, signal processing, sports analytics, and weather forecasting. While time-series forecasting may seem universally applicable, there are limitations that developers need to consider, including understanding the data model and identifying stable trends within historical data. Key factors in time-series forecasting include the amount of data, data quality, seasonality, trends, and unexpected events. Several popular forecasting methods exist, such as time-series decomposition, time-series regression models, exponential smoothing, ARIMA models, and neural networks. Each method has its benefits and drawbacks, and choosing the right one depends on the specific situation and type of data. Neural networks offer robustness to noise, nonlinear support, multivariate inputs, and multi-step forecasts, making them a valuable option for time-series forecasting. TBATS models can handle complex seasonalities with no seasonality constraints, but may be slow when calculating predictions. Overall, time-series forecasting is a powerful method for predicting future trends and values in time-series data, and understanding its applications and limitations is crucial for informed decision-making.