Are you seeking a vacancy for a Machine Learning engineer? With almost all of the companies going from traditional to technical, the scope for Machine Learning has increased exponentially. There are lucrative options in Machine Learning for Time Series Forecasting with Python. Time Series Analysts or engineer roles are expected to pique the interest of aspirants willing to opt for options in technical fields. 

Diving into a new field must accompany familiarity with the topic and important concepts of the field. Not only does it help mental preparedness, but it also strays away the fear of the unknown or challenges. So, we bring you a comprehensive guide encompassing different aspects of Time Series Forecasting Machine Learning has in store! 

Here’s what we will cover in the article:

• What is Time Series Forecasting?
• Time Series Forecasting Machine Learning
• Components of Time Series Forecasting
• Time Series Forecasting Modeling
• Applications of Machine Learning Time Series Forecasting
• Nail the Next Machine Learning Time Series Forecasting Interview with Interview Kickstart
• FAQs on Machine Learning Time Series Forecasting 

What is Time Series Forecasting?

Time series forecasting refers to the process of analysis of already available time series data to predict future events. The available data can be historical or of current time, where the frequency can vary from hours, days, weeks, months, to quarter, semi, or annual period. A powerful contribution from Artificial Intelligence and Data Science, it is critical for observations and strategic decision-making for businesses, governments, scientists, and other professions. 

Time Series Forecasting Machine Learning

Time series forecasting includes collecting observations over different time periods along with the characterization and comprehension of the observed data. It determines the reason for the occurrence of alterations or modifications, which helps to create Machine Learning based prediction models. The specialized Machine Learning models, discussed later, use facts or above observations for predicting future values further utilized in resource and time allocation. The models evaluate and observe both current and historical data carefully using the best-fitting model in time series forecasting to anticipate future outcomes.

Components of Time Series Forecasting

Time series forecasting involves several components that are important to be understood for building accurate and effective forecasting models. They are enlisted as follows:

1. Trends: The component is comprehended to determine the lagging and declining time series pattern illustrated in linear modes.
2. Cyclicity: The element is incorporated to locate the recurring variations in the time series. The element further aids in describing the cyclic nature of the located recurring variations.
3. Seasonality: The element is used to focus on the recurring behavior of the time series over different time periods.
4. Noise: It is incorporated into the model to allow non-systematic elements of the time series, which vary from the standard model values.

Note: While seasonality focuses on fixed intervals, cyclicity extends beyond seasonal patterns and is not tied to fixed intervals. 

Time Series Forecasting Models

A wide range of models used for time series forecasting are stated as follows:

ARIMA/SARIMA

ARIMA, an abbreviated form for Autoregressive Integrated Moving Average, is a combination method used to generate composite time series models. It handles seasonal and trending parameters and allows the implementation of autoregressive and moving average terms for data autocorrelations. On the other hand, SARIMA elaborated as Seasonal Autoregressive Integrated Moving Averages, incorporates forecast errors and/or past seasonal value’s linear mixture to widen the ARIMA applications. 

Recurrent Neural Network (RNN)

It is simply neural networks with enhanced memory that have the capacity to anticipate time-dependent targets. In order to divide the next time step, recurrent neural networks recall the state of input that was earlier obtained. Currently, several modifications have been made to recurrent networks to use them in multiple fields. 

Multi-Layer Perceptron (MLP)

In certain cases, MLP is used widely with respect to any feedforward Artificial Neural Network (ANN). MLP also refers to networks composed of multiple layers of perceptrons, indicating ambiguous usage of the term. The multiple layers are composed of one input layer and an output layer covering one or more hidden layers. They are used to capture complex relationships within the data. 

Naive Model

It offers a simple method with minimum effort to prepare a forecast. Generally, the two methods are implemented: random walk and seasonal random walk. The random walk uses recent values for forecasting the next period. The seasonal random walk uses the value of a specific period in the previous time for making predictions in the same period of the present time. 

Linear Regression Method

A specific statistical method, linear regression, is applied for predictive modeling. Time series forecasting with Machine Learning and statistics includes offering equations with independent variables that affect the target within a different time period.

Applications of Machine Learning Time Series Forecasting

Time series forecasting in organizations and businesses dealing with huge and constant data or requiring adjusting the alterations during the operational timings can be considered time series forecasting. Here are some of the common applications:

• Web Traffic Forecasting: Companies can use common data to predict the traffic rates at the time period of requirement. Time series forecasting will take up the input and utilize traffic rates at competitors to provide the output. 
• Weather Prediction: Typically, all the weather-related information is collected from worldwide weather stations. Machine Learning later evaluates the information and provides valuable insights from the input data to predict future forecasts based on statistical dynamics.
• Scientific Analysis Forecasting: The scientific data is generated after an analytical cycle where analysis takes time. Machine Learning and deep learning simplify and speed up the refining and launching process of scientific ideas.
• Stock Price Forecasting: Companies can insert historical stock price data, including spikes and declines, on a regular or atypical basis. The provided output will enlighten you with accurate forecasts for reasonable forthcoming stock price movements.
• Sales and Demand Forecasting: The data inputs of the customer’s behavior, such as seasonal influence and purchase and demand history, are taken. Machine Learning approaches enable the discovery of purchases in high demand and pinpoint their deployments in a wide range of markets.

Nail the Next Machine Learning Time Series Forecasting Interview with Interview Kickstart

Time series forecasting is a comprehensive analysis of current and historical data. Offering valuable insights to predict future behavior, Machine Learning approaches are widely applied in companies. It leads to increased requirements for candidates proficient with Machine Learning skills. Exhibiting the possession of all qualities requires the presence of behavioral, technical, and interpersonal skills. 

At Interview Kickstart, we know the pressure to crack an interview. Therefore, we provide a comprehensive course covering all the aspects necessary to crack the interview at FAANG companies. Training the aspiring candidates here are top recruiters from FAANG companies, who are experts in providing personalized mentoring sessions. Do you want to crack your next interview at FAANG? Register for our webinar for free. 

FAQs About Time Series Forecasting Machine Learning

Q1. How does LSTM work for time series forecasting?

LSTM or Long Short-Term Memory works to replace the vanishing gradient problem. To accomplish this, it uses specialized gating mechanisms to control the flow of information. The input, output, and forget gates control the weights and assist in creating long-term memory functions.

Q2. Is LSTM a Machine Learning algorithm? 

Yes, LSTM is a Machine Learning algorithm. To be specific, it is a type of Recurrent Neural Network suited to learning long-term dependency on sequential data. 

Q3. Is time series forecasting the same as regression?

No, though both are methods of predictive analytics, there lies a difference between the two. While time series analysis evaluates the change of variables over time, regression analysis explores the relationship between dependent and independent variables. Further, time series forecast is extrapolation, while regression is interpolation.

Q4. Why use RNN for time series forecasting?

Recurrent Neural Network or RNN is a good choice for time series forecasting as it allows the capture of temporal dependencies in the data due to the presence of implicit memory. 

Q5. What is the Python package for time series forecasting?

AutoTS and Tsfresh are some Python packages for time series forecasting.