What is TimeSeriesForecasting?
Time series forecasting is the practice of using past data points that are ordered in time to predict future values. It looks at patterns like trends, seasonality, and cycles to guess what will happen next.
Let's break it down
- Time series: a list of numbers (or measurements) recorded one after another over time, like daily temperatures.
- Forecasting: making an educated guess about something that hasn’t happened yet.
- Using past data: the method looks at what happened before and assumes similar patterns will continue.
- Patterns (trend, seasonality, cycles): regular ways the data moves up, down, or repeats over time, which the model tries to capture.
Why does it matter?
Because it helps people and businesses plan ahead, avoid surprises, and make smarter decisions by anticipating future demand, risk, or performance.
Where is it used?
- Predicting sales or inventory needs for retail stores.
- Estimating electricity or water consumption for utilities.
- Forecasting weather conditions such as temperature or rainfall.
- Anticipating stock prices or financial market movements.
Good things about it
- Turns historical data into actionable future insights.
- Can be automated and run regularly with little human effort.
- Supports many industries, from finance to healthcare.
- A variety of simple to advanced models are available, fitting different skill levels.
- Improves resource allocation, saving time and money.
Not-so-good things
- Requires a sufficient amount of clean, consistent historical data.
- Assumes past patterns will keep repeating, which isn’t always true.
- Sensitive to outliers or sudden changes (e.g., a pandemic).
- More sophisticated models can be complex to set up and fine-tune.