Forecasting is a critical skill for any business professional. Making wise decisions that will help your business succeed requires the ability to predict future trends and patterns, whether you work in sales, marketing, or operations.
If you’re applying for a job that calls for forecasting abilities, you should prepare for a range of inquiries about your background and method. To assist you in preparing for your upcoming interview, we’ve compiled some of the most typical forecasting interview questions and sample responses in this guide.
- What is Moving Average?
- What is Auto Regression (AR)?
- What is the difference between ARMA and ARIMA?
- Can you explain RNN and LSTM, and when you use each for TSA?
- Can you explain Dynamic Time Warping?
- Can you explain the Hidden Markov Model?
Time Series Theory Q&A | Analytics Interview
The cross variance function is only a function of lag h. They are both stationary.
30) The following MA (3) process, where t is a zero mean white noise process with variance 2, has the following ACF values:A) ACF = 0 at lag 3; B) ACF = 0 at lag 5; C) ACF = 1 at lag 1; D) ACF = 0 at lag 2; E) ACF = 0 at lag 3 and at lag 5Solution:
A) Multiple points on the same series observed at different times B) two points on the same series observed at different times with quadratic dependence C) multiple points on the same series observed at different times with linear dependence D) multiple points on the same series observed at different times with quadratic dependence
As the time intervals between the observations get closer together, clusters of observations are frequently correlated with increasing strength. This must be true because, unlike classification or regression, time series forecasting is based on historical observations rather than the data that is currently observed.
The moving-average (MA) model is a popular method for modeling univariate time series in time series analysis. According to the moving-average model, the output variable is linearly dependent on the current value as well as various previous values of a stochastic (imperfectly predictable) term.
An illustration of a modeling technique that uses historical data to predict future events is ARIMA, which stands for Auto Regressive Integrated Moving Average. To forecast future results, the model employs three components: the moving average, the integrated component, and the autoregressive component. In my previous position, I applied an ARIMA model to determine how our company could increase sales over the following six months. ”.
Example: “Entropy is a measure of disorder within a system. I used entropy in my previous position as a business analyst to determine which types of data were most likely to occur based on their degree of disorder. For instance, if a month’s sales figures had low entropy, they were more likely to be repeated than those from months with higher entropy. ”.
A technique for forecasting future events using historical data is called time series forecasting. You can demonstrate your understanding of the subject and how it can be used in professional settings by responding to this question. Define time series forecasting in your response by describing what it is and how it works. If you can, give an instance of when you used this procedure in your previous position.
“Yes, I have dealt with missing values in my datasets before,” for instance In this case, my initial attempt is to determine why the data was missing. If it’s due to a technical error, I’ll look for additional sources of data to fill in the blanks. However, if no records are available, I will estimate the value using historical trends or comparable metrics. ”.
The most effective algorithm to use will depend on my goals, for instance. For instance, because linear regression is good at spotting patterns in data, I would use it to forecast future trends based on historical data. Neural networks would be used to create a model that could adapt to new information. When I need to make predictions about intricate relationships between numerous variables, neural networks are also helpful. ”.
FAQ
What are the 4 components of time series?
- Secular trend, which describe the movement along the term;
- Seasonal variations, which represent seasonal changes;
- Cyclical fluctuations, which correspond to periodical but not seasonal variations;
- Unusual variations, which are additional non-random sources of series variations
What are the 3 key characteristics of time series data?
- Trends.
- Seasonal and nonseasonal cycles.
- Pulses and steps.
- Outliers.
What are the 3 components of time series?
The trend (long-term direction), the seasonal (systematic, calendar-related movements), and the irregular (unsystematic, short-term fluctuations) components of an observed time series can all be separated out.
What is time series techniques?
A particular method of analyzing a series of data points gathered over a period of time is called time series analysis. Instead of just recording the data points intermittently or randomly, time series analysts record the data points at regular intervals over a predetermined period of time.
