Time Series data is generally present in the field of Data Science. Whether to analyze trends and forecasting company's trends.
In this repository, I have explained how to start working with time series data, and how to visualize the time series data using Python.
This repository is further divided into 4 sub-chapters -
- Chapter-1 : Introduction to Time Series Plots
- Chapter-2 : Plotting Rolling Models and Aggregate Values Models
- Chapter-3 : Autocorrelation, Partial Autocorrelation Plots and Seasonality, Trend and Noise Plots in Time Series Data
- Chapter-4 : Work with Multiple Time Series Data
1. CHAPTER-1
In this chapter, I have explained how to leverage basic plottings tools in Python, and how to annotate and personalize your time series plots.
2. CHAPTER-2
In this chapter, I have explained about deeper understanding of our time series data by computing summary statistics and plotting aggregated values from our data.
3. CHAPTER-3
In this chapter, I have explained about autocorrelation and partial autocorrelation plots.
And, also explained how to detect seasonality, trend and noise in time series data.
4. CHAPTER-4
In the field of Data Science, it is common to work with multiple time series simultaneously.
In this chapter, I have explained how to plot multiple time series at once, and how to discover and describe relationships between multiple time series.