A time series analysis is a method of data analysis that involves the observation of a variable's trend over time. Using this data organizations are able to identify how a certain product has been doing over time as well as identify the trend of its rise and falls over time and be able to answer the "why" question. This could help know at what season a product has the best sales so at to ensure they have it stock by the next season sales are expected to rise. This is important important as it helps the company do timely re-stocking and hence they are able to maximize sales and profits.
Additionally, a time-series analysis helps an organization detect when a commodity was at its lowest and this can help them understand why it was so by reviewing the timelines and see circumstances that might have led to that. This makes a time-series analysis an important tool for business forecasting.
The best way to get the most of a time-series analysis is by visualizing it using eg with a scatter-plot, line chart or any other appropriate data visualizing tool at your disposal
Steps to a Time-Series Analysis
1. Data Collection
Gather all the relevant information you need for your analysis may it be sales records, website logs, order histories, date and time stamps any relevant information for your analysis should be available
2. Data Cleaning and Preprocessing
This is the most important stage as this helps prepare the data you will use in you analysis. Data pre-processing involves the filling of null values, removal of duplicate values, data type conversion, dealing with outliers and standardizing the data to the best workable format possible. This helps to prevent outputting wrong analysis during your analysis based on the rule of Garbage In Garbage Out
3. Data Visualization
After you data is ready, use data visualizing tools to plot your data to visuals that will help you get actionable insights from your data. This will enable you notice major trends, outliers as well as patterns that are important in answering your questions
4. Time Series Decomposition
A time series has 3 components : Trend, Seasonality and Residue(noise)
Trend=> Identifiable pattern of frequency of events. Seasonal=> Trends that happen after specific amount of time Residual=> Patterns that are out of trends and not in season
5. Model Selection
Choose an appropriate Time-Series model to help model your data. The major models include:
- Autoregressive Integrated Moving Average (ARIMA): For stationary data.
- Seasonal Decomposition of Time Series (STL): For data with seasonality.
- Exponential Smoothing (ETS): Another method for forecasting time series data.
Data from the model should be evaluated so as to determine its correctness.
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