How To Obtain Accurate Results During A Time Series Analysis
For as long as humans have been able to articulate it, they’ve wanted to predict the future. The future was usually just randomly guessed by seers and astrologers, but today we can predict it with science. Here is how to get the most accurate readings using time series analysis, and correctly “predict” the future.
Adjust for Seasonal Effects
A seasonal effect is a recurring calendar effect. For example, people get a higher electricity bill in the winter because they heat up their homes more, and agricultural crops turn out a high profit in autumn because it’s harvest time. This doesn’t say much about the heating or crops themselves.
If you notice seasonal effects in your analysis, they have to be analyzed and their influences must be removed. Seasonal effects can hide a series’s key characteristics.
Avoid Look-Ahead Bias
We fall victim to the look-ahead bias when information that wasn’t available at the time of the analysis is used, heavily skewing the results. When you backtest the simulation, you won’t get an accurate result. This is why you must be careful to only select information that you already had at the beginning of the simulation, but using data science fundamentals to avoid look-ahead bias should be an effective enough preventative measure. Look-ahead bias is dangerous because we alter the analysis to give us the results we already anticipated or desired.
Stationarize Your Analysis
A stationary time series is ideal for time series modeling. When a time series is stationary, its variance and mean are constant and its statistical properties are not dependent on time passing.
For example, a frictionless pendulum swings back and forth with the same frequency and amplitude for as long as you let it. If you apply a force to it, like the friction of air, the frequency or amplitude of its swing would change, making it a non-stationary process.
We default to stationarity when it comes to time series analysis, so non-stationary data is converted to stationary. You can test whether your process is stationary using the Dickey-Fuller test.
Replacing Missing Values Accurately
If there are missing values in your time series forecasting data, they need to be replaced. But you can’t just throw in random values, or just values that you think are close enough to what they would have been.
There are a few commonly used methods to replace missing values as accurately as possible. For example, with the linear interpolation method, you draw a line connecting the point right behind your missing value to the one right in front of it. Data that shows a distinct seasonality can be replaced by the average of that data point in the previous and next seasonal period. This is called seasonal and linear interpolation.
Time series analysis has been used to solve a myriad of problems from a variety of diverse fields of study. From predicting stock prices to predicting peak electricity consumption times, this modeling technique has resolved many industry challenges. Hopefully, you now know what to look out for to get the most accurate predictions using this time-based data analysis.
