Hi,
In today’s class I learned about time series analysis. It’s commonly used to forecast future events based on past trends, identify patterns, and analyze the effects of certain decisions or events. There are several key components and methods in time series analysis which I learned in today’s class. those are as follows:-
- Trend Analysis: This involves identifying the underlying trend in the data, which could be increasing, decreasing, or constant over time.
- Seasonality: This refers to patterns that repeat at regular intervals, such as weekly, monthly, or yearly. Seasonality analysis helps in understanding and adjusting for these regular patterns.
- Stationarity: A time series is stationary if its statistical properties like mean, variance, and autocorrelation are constant over time. Many time series models require the data to be stationary.
- Models for Time Series Analysis: Common models include ARIMA (Autoregressive Integrated Moving Average), SARIMA (Seasonal ARIMA), and more advanced machine learning models like LSTM (Long Short-Term Memory) networks.
We also had a chance to look at the economic indicators data and learn practically about time series analysis. By next class, I will be selecting a dataset from data.boston.gov and discuss in the next class.