Must-Know Time-Series Analysis Techniques for Data Analysts

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Must know time-series analysis methods to know as a data analyst🔥 A thread👇

Note: This thread is derived from the below article published on @DataKwery Please check out the article and courses to learn time series using the link provided below👇 datakwery.com/post/time-series-analysis-techniques-for-data-analysts/?utm_source=avi&utm_medium=blog

1. Time-series data understanding & pre-processing Time series data pre-processing is the key step to converting raw data into processed data that can be effectively analyzed and help in gaining critical insights out it. Convert 'object' dtype to 'datetime' dtype

2. Time-series decomposition The time-series data can be modeled as an addition or product of trend, seasonality, cyclical, and irregular components. The additive time-series model is given by Yt = Tt + St + Ct + It

The multiplicative time-series model is given by Yt = Tt x St x Ct x It Where Tt = Trend component St = Seasonality Ct = cyclical component It = irregular component Let's look at the code below

The output of the above code👇

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3. Time-series data analysis and visualization 3.1 Comparative analysis of the stock prices of companies within the same industry Comparative analysis of stock prices refers to comparing the performance of one or more stocks in a given market or industry over a specific period

The goal of this analysis is to understand how stocks are performing relative to each other and to identify trends, patterns, and relationships between them.

3.2 Growth of the stock prices over 5 years The growth of stock prices refers to the increase in the value of a stock over some time. It is an important metric for investors to consider when making investment decisions, as it can indicate the potential return on investment.

The commonly used formula for calculating the growth of stock price is as below: Rate of return = (Ending price — Starting price) / Starting price Rate of return over a period of 5 years

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4. Forecasting using ARIMA models ARIMA (AutoRegressive Integrated Moving Average) models are a class of time-series forecasting models that are commonly used for modeling and predicting future values of time-series data.

ARIMA models capture the autoregressive element, the difference element, and the moving average element of time-series data to make predictions.

5. Stationarity test using statsmodels library The AR and MA models can only be used if the time series is stationary. the I elements help to build forecasting models on non-stationary time series.

ARIMA models are used when the time-series data is non-stationary. time-series data is called stationary if the mean, variance, and covariance are constant over time.

We can set up the null hypothesis and alternate hypothesis as below to test dicky-fuller test. H0: Time series is non-stationary Ha: Time series is a stationary If the p-value is less than 0.05 then we will reject the null hypothesis and accept the alternative hypothesis.

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