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Time Series Forecasting: Types, Examples - StarAgile

StarAgilecalenderNovember 21, 2022book16 minseyes2053

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What is Time Series Forecasting?

Time series forecasting is a common technique used in the field of data science for generating business predictions. Making scientific projections based on data with historical time stamps is known as time series forecasting. It involves generating models through historical study, using them to draw conclusions and guide strategic decision-making in the future.

The fact that the future result is unknown at the time of the task and can only be anticipated through thorough analysis and evidence-based studies is an essential distinction in time series forecasting. The ability to analyse how variables change over time differentiates time series data from other types of data. In other words, time is a key factor since it reveals how the data changes throughout the data points as well as the outcomes. It provides an additional information source and a predetermined hierarchy of dependencies between the data.

Time Series Forecasting is Relevant Universally

Several industries use time series forecasting for the highest level of assurance. Weather and climate, astronomy, finance and economics, healthcare, engineering, retail, business, environmental studies, and social studies are among the many sectors which employ this technique to forecast potential outcomes. Analysts use time series data to present results in terms of level, trend, seasonality, cyclic patterns, and irregularity.

Time series forecasting is applicable in many organisational functions, such as production, marketing, sales, finance, and logistics, to measure potential technical costs and customer demand. Time series models come in a variety of shapes and can depict various stochastic processes.

Examples of Time Series Forecasting

To give the concepts of time series forecasting additional context, the following examples from various industries are given:

  • Estimating a stock's daily closing price.
  • Estimating a warehouse's daily inventory of a product category.
  • Predicting a state's unemployment rate each quarter.
  • Estimating the daily average price of fuel.

Not all data may be categorised as time series data, even if they include time or date values as characteristics. When working with the unforeseen and the unknown, there are limitations. Things that are random will never be forecast with any degree of accuracy. Consider an example — even though data for each lottery winner can be tracked every week, one can never predict who will win the next drawing.

Any set of data that is suitable for time series forecasting must have observations made over a regular, continuous period.

Types of Time Series Models

Based on validated historical data, a specific time series model is used to forecast events. Moving Average (MA), Autoregressive (AR), and Autoregressive Integrated Moving Average (ARIMA) are a few common examples. An ideal model must be selected based on each time series because different models will produce different results for the same set of data.

Each of the following categories of the time series model offers a specific outcome:

  • Decomposition: Since time series data can display a wide range of patterns, it is frequently beneficial to divide a time series into sections, each of which represents an underlying pattern category.
  • Smooth-based: A statistical approach used in time series forecasting, helps eliminate outliers from a time series data collection to enhance the visibility of a trend. Some kind of random variation is present in every collection of data gathered over time. Data smoothing reveals underlying trends and cyclical components while removing or reducing random variance.
  • Moving-Average: States that the output variable is linearly dependent on the current and various historical values of a stochastic (unpredictable) component
  • Exponential Smoothing: Performs functions of the smooth-based model using an exponential window function. It is a simple process for determining anything depending on prior assumptions made by the analyst, such as seasonality.

Several models are built based on the type of data available and the forecasting objective.

List of Time Series Forecasting Models Based on Decomposition Model

  • Additive Decomposition Model
  • Multiplicative Decomposition Model

List of Time Series Forecasting Models Based on MA Model

  • Autoregressive (AR) Model
  • Moving Average (MA) Model
  • Autoregressive Moving Average (ARMA) Model
  • Autoregressive Integrated Moving Average (ARIMA) Model
  • Seasonal Autoregressive Integrated Moving Average (SARIMA) Model
  • Vector Autoregressive (VAR) Model
  • Vector Error Correction Model (VECM)

List of Time Series Forecasting Models Based on Exponential Smoothing Model

  • Single Exponential Smoothing (SES) Model
  • Double Exponential Smoothing (DES) Model
  • Triple Exponential Smoothing Model (the Holt-Winters method)

Additionally, Prophet and DeepAR are two more well-known time series forecasting software programmes. Meta created the Prophet additive model, which fits non-linear trends to seasonality effects like daily, weekly, yearly, and holiday trends. Amazon created a programme called DeepAR that makes it possible to forecast time series using recurrent neural networks.

Identify the Purpose of Time Series Forecasting

Before choosing a certain time series model, the foremost critical task of time series forecasting is ‘understanding the purpose’. Analysts need to question the following to focus on the elements of the predictive modelling issue:

  • Quantity of data that is accessible: The more the data, the more benefits; providing more opportunities for exploratory data analysis, model testing and tuning, and model quality.
  • Duration of forecasts time horizon: Shorter time horizons are preferred to forecast with more assurance than longer ones.
  • Forecast modification frequency: Forecasts may need to be revised often over time or they may just be produced once and then remain unchanged (updating forecasts as fresh information comes in often results in more precise predictions).
  • Forecast temporal frequency: Forecasts can frequently be achieved at lower or higher frequencies, enabling the use of data up-sampling and down-sampling (this, in turn, can provide advantages while modelling).

Time Series Analysis in Python

Python offers a variety of simple-to-use libraries and tools for time series analysis. With just a few lines of code, ARMA, ARIMA, and SARIMA models may all be created using the stats module in Python. When performing time series analysis in Python, one can quickly run multiple experiments using several models in the same script or notebook because all these models are accessible in a single library.

This data science online course intends to introduce Python-based mathematical computing, web scraping, data analysis, data visualisation, and data wrangling. Additionally, it discusses natural language processing and machine learning (ML) principles (NLP).

Time Series Analysis in R

A time series analysis in R is described as a collection of values, each of which is timestamped and is monitored across regular time intervals (daily, monthly), such as in the case of weather forecasting and stock analysis. The ts() method is used by R as a basis distribution to generate the time series object, which contains the time series data.

During the training, concepts such as hypothesis testing, Kmeans, DBSCAN Clustering, and the Apriori algorithm are covered.

The design of the Data Science with R Certification Online Course covers data science, machine learning techniques, and the analysis and visualisation of various data sets. With a specific focus on decision trees, naive Bayes, and random forest, detailed explanations of machine learning methods are provided.

Take the Next Step

Grab the chance to learn the concepts of time series analysis through practical activities. Enrol in the Data Science with Python or Data Science with R course to better comprehend time series forecasting. Understand the concepts from industry-renowned mentors through live sessions. Attend time series model workshops and learn how they function. Be a part of real-life industry projects using Python or R for time series analysis. Upon successful completion of the Data Science online course, receive an authorised certification.

To learn more about data science principles in-depth, have a look at the list of the Best Data Science Course.

Frequently Asked Questions (FAQs)

1.  What is the need to learn Data Science with Python?

To begin quickly with technical and scientific computing activities, the data scientist must have access to the NumPy and SciPy libraries. Thus, knowing Python gives access to several data science libraries, such as NumPy and Pandas, making it the ideal language for implementing algorithms and developing applications.

2.  Is it important to learn R for Data Science?

Studying R can help to expand the skillsets of professionals. If one wants to explore the inner data scientist, this is a fantastic place to start.

3.  Is coding experience required to take the Data Science with Python or Data Science with R Course?

Because the training begins with the fundamentals, one does not need prior coding knowledge to learn Data Science with Python Course or Data Science with R.

4.  R vs Python: which language should data scientists learn?

Each language has advantages and disadvantages. R's rich built-in libraries and capabilities, according to some experts, make it better suited for scientific computing and statistics.

5.  What hardware and software need to be installed to take the Data Science online course?

An i3 processor, 4 GB of RAM, and a steady internet connection are required. Highly recommended is an i5 processor with 8 GB of RAM.

 

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