ROM (Read-Only Memory):-  are both crucial types of memory in computers, but they serve different purposes.

RAM:- is a type of volatile memory, meaning it loses its data when the power is turned off. It temporarily stores data that the CPU needs while performing tasks, making it essential for running applications and processing data quickly.

For example, when you open a program, it loads from the hard drive into RAM, allowing for fast access and smooth operation.RAM is flexible and can be read from and written to multiple times.

ROM, on the other hand, is non-volatile memory, meaning it retains its data even when the power is off. It permanently stores critical data needed for the computer to boot up and function, such as firmware or the BIOS. Unlike RAM, ROM is read-only, meaning the data written on it cannot be easily modified or erased.

This makes ROM ideal for storing software that doesn’t need frequent updates. In summary, RAM  used for temporary, fast-access storage that supports active processes, while ROM provides permanent storage for essential system instructions.

Time series analysis is a way to study data points collected over time to identify patterns and trends. Imagine you record the temperature at noon every day for a month. The list of temperatures is your time series data.

Here are some common methods used in time series analysis:

1. Moving Averages: This method smooths out short-term fluctuations by averaging the temperatures over a few days. For example, if you average the temperatures of the last 3 days, you get a smoother trend line.
2. Exponential Smoothing: Similar to moving averages, but it gives more importance to recent temperatures, making it more responsive to changes.
3. ARIMA (AutoRegressive Integrated Moving Average): This complex method uses past temperatures to predict future ones, removes trends, and averages out fluctuations.
4. Decomposition: This method breaks down the temperature data into components like overall trend, seasonal patterns (e.g., weekly cycles), and random noise.
5. Seasonal Decomposition of Time Series (STL): This technique focuses on identifying and separating seasonal patterns from the data.

For example, if you notice that temperatures tend to rise every weekend, decomposition can help you understand this pattern. These methods help in making informed decisions by analyzing past data and predicting future trends.