The world around us is changing fast, and we need to understand the seasonality behind the way the world works.
Let’s understand this with the help of some examples.
Imagine analyzing rainfall data, predicting crop growth, or even the fluctuations in air ticket pricing during carnival to Rio. One can easily decipher that there is a certain amount of seasonality to these data points, and they go in cycles.
These seasonal aspects also have a direct bearing on all the other facets like sales of related products. Even the stock market is susceptible to seasonal upheaval. These give rise to the relevance of the analysis of the time-series data. In simple terms, this means collecting data at a regular interval with the time stamp on it to understand how the data varies with changing time. This analysis helps in predicting whether the data is stationary, is there a seasonality, or are they autocorrelated.
Suppose the metallurgy department wants to predict the amount of rainfall that is going to happen this year based on the historical data. This is called forecasting and is a bit more complicated than a regular modeling task. To do this, one needs to first visualize the time series, make it stationary, and find whether there is an autocorrelation or not. Based on that, the model that fits them the best needs to be decided and applied. Once all these have been completed, the predictions can be made, and rainfall can be forecasted with some accuracy.
Let us dive deeper into the various models which play a crucial role in Time Series analytics.
The most popular ones are the ARIMA/SARIMA model, Seasonal Decomposition, exponential smoothing, and GARCH. The most popular method is the simple exponential smoothing method. The principle is to analyze the past observations on the basic weighted average, which decreases exponentially as we go back in time. In case the data shows some seasonality, then the whole equation needs to be broken down into the – the seasonal, trend, and the remainder component.
The ARIMA/SARIMA model is the short form of Auto-Regressive Integrated Moving Average and Seasonal ARIMA. This model combines the linear combination of the past variable values along with the error values in the forecast. Another methodology is the GARCH methodology, which factors in the fact that the error terms will change over some time.
While the modeling process for time series analytics is extremely complicated, there are a couple of basic steps that need to be followed to make it simple.
The first step is to identify the correct problem statement. For instance, the problem statement could be predicting stock prices. Stock prices of various companies vary over time and are the best fit for the time series analysis. Another example could be to understand electricity usage over a period or predict air quality – for all these cases we need to employ Time Series Analysis.
Once the problem statement is identified, the next step is to decide on the tool which can be leveraged for doing the analysis. By tools, we mean R, Python, etc. The choice depends upon the Data practitioner’s level of comfort. Once the tool has been zeroed down, the Data practitioners download all the libraries that are required to do the analysis.
The next step is to gather all the relevant data from all the internal as well as external sources. It is possible that the data sets could have duplicities and may need to be scrubbed and cleaned for further analysis.
Once the data sets are analytics-ready, the next step is to do the exploratory analysis. For this, the data points are first represented on a graph. Any time-series data, by its basic nature, will have spikes and drops in its graph plots. It is essential to employ the exponential smoothing process to cut out the noise. In some instances, employing it just one time may not effective, and it may need to be carried out more than once to smoothen it effectively.
Post that, one needs to employ the ARIMA, SARIMA model. This step includes defining the parameters and generating a list of possible combinations. SARIMA can be used to train the system to find out the best possible combination and the model. By leveraging this model, one can find out how effective it is to predict the numbers. If it lies within a designated tolerance limit, it is leveraged for future prediction and forecasting.
When data changes over some time, it can be used to predict the future. Some real-world use cases of this include,
Consumers also show seasonality and cyclical nature in their buying patterns. By understanding the time-series aspect of it, organizations can plan better for the future. With their understanding of how much time it takes to manufacture a particular product and how its demand is going to vary over time, they can better plan the production and operation. This also has a severe impact on various aspects, such as inventory planning, distribution, supply chain, etc. have severe implications.
Is your enterprise ready to leverage the power of data to predict the future?