Time series forecasting is like predicting the future based on how things have changed in the past. Imagine you have a timeline of events, and you want to figure out what might happen next. This is used in many areas, like predicting stock prices, estimating how much of a product people will want to buy, forecasting energy usage, or even predicting the weather.
Stationarity: We often assume that the way things have been changing will keep happening the same way. This makes it easier to make predictions.
Components of Time Series: Imagine the data as having three parts – a long-term trend (like a steady increase or decrease), repeating patterns (like seasons changing), and random ups and downs that we can’t explain (we call this noise). C
Common Models: There are different ways to make predictions. Some look at the past data’s trends and patterns, like ARIMA. Others, like LSTM and GRU, are like smart computer programs that learn from the past and make predictions.
Evaluation Metrics: We use tools to check if our predictions are accurate. It’s like making sure our guesses about the future match up with what actually happens.
Challenges: Sometimes things change in unexpected ways, or there are unusual events that we didn’t predict. Adapting to these changes is one of the tricky parts.
Applications: This forecasting tool is handy in many fields. For example, it can help predict how much money a company might make, how many products they need to produce, or how much energy a city might use. In a nutshell, time series forecasting helps us plan for the future by learning from the past. It’s like looking at patterns in a timeline to make smart guesses about what comes next.