# 60assignment 2

## 60.1 Smoothing

In this assignment, you will delve into the application of different smoothing techniques on time series data. Utilizing meteorological data, your task is to create a series of plots that demonstrate the effects of various smoothing methods.

### 60.1.1 1. Comparative Smoothing Methods Analysis

• Goal: Showcase three smoothing techniques – Rolling Average, Savitzky-Golay, and Resampling – on the same time series data.
• Task: Overlay these methods over the actual data in a single plot. Ensure each method uses the same window size for consistency. Describe in a few lines the differences you see.
``# code goes here``

### 60.1.2 2. Rolling Average Window Size Impact

• Goal: Analyze the effect of varying window sizes on the Rolling Average method.
• Task: Produce a plot with three lines, each representing the Rolling Average with a different window size. Describe in a few lines the differences you see.
``# code goes here``

### 60.1.3 3. Savitzky-Golay Polynomial Order Variation

• Goal: Investigate how changing the polynomial order affects the Savitzky-Golay smoothing method.
• Task: Create a plot with three lines, where each represents the Savitzky-Golay method with a different polynomial order. Describe in a few lines the differences you see.
``# code goes here``

### 60.1.4 4. Kernel Shape Influence in Rolling Mean

• Goal: Explore the impact of different kernel shapes on the Rolling Mean.
• Task: Generate a plot displaying three lines, each using a different kernel shape in the Rolling Mean. We encorage to use unique kernel shapes that we did not showcase in class. See this list of kernels. Describe in a few lines the differences you see.
``# code goes here``

### 60.1.5 5. Moving Average with Confidence Interval

• Goal: Plot a Moving Average along with a 75% confidence interval.
• Task: Design a plot illustrating both the Moving Average and its 75% confidence interval.
``# code goes here``