1、Table of ContentsHow AI Improves Business Forecast AccuracyDifference Between Forecasting&PredictiveModelingUse ases For Machine Learning Forecasting For BusinessFINANCIAL FORECASTINGSUPPLY CHAIN FORECASTINGPRICE PREDICTIONDEMAND&SALES FORECASTINGFRAUD DETECTIONKey Machine Learning Forecasting Algor

2、ithmsREGRESSION ALGORITHMSDEEP LEARNING ALGORITHMSTREE-BASED ALGORITHMSGAUSSIAN PROCESSESAUTO-REGRESSIVE ALGORITHMSEXPONENTIAL SMOOTHINGHow to Apply Machine Learning ForecastingChallenges of ML ForecastingBusiness forecasting is imperative for making balanced financial and operationaldecisions.Its i

3、mpact across industries has grown in recent years due to the waycompanies build data-driven strategies and rely on data.But lets find out what isneeded for efficient forecasting and why machine learning models have all theprerequisites for enhancing business intelligence.1In this article,well go ove

4、r the principles of ML forecasting functioning and thebenefits it can bring if used for business purposes.Also,we will highlight thedifferences between machine learning forecasting models,from regression toexponential smoothing.How AI Improves Business Forecast AccuracyThanks to forecasting,companie

5、s are able to better serve clients and shiporders,instead of running out of stock.This leads to a huge impact on sales andcustomer satisfaction.For example,knowing the demand brings an ability tomanage logistics and track inventory costs,or even predict ROI for a newproduct.Therefore,ML forecasting

6、models allow organizations to enhance theirAI maturity,and more importantly,to solve business tasks by looking at existingdata.Nowadays,the volume of data from markets,industries,and users isskyrocketing.FinancesOnline reveals that the world will produce and consume94 zettabytes in 2022.Such growth

7、fuels the training of ML models,making themmore robust and accurate.According to Market Research Future,the ML marketshare is projected to reach$106.52B by 2030,with a CAGR of 38.76%during theforecast period of 2020-2030.With increasing market share(caused by evolvingcloud-based services and growth

8、in unstructured data)comes new opportunitiesfor building forecasting models.So,lets figure out how these models improvebusiness forecast accuracy and why they are more efficient than traditionalapproaches.ML forecasting rests on an enormous amount of information,which can beanalyzed to achieve accur

9、ate predictions and high performance rates.Unliketraditional forecasting approaches,machine learning allows companies toconsider numerous business drivers and factors,and for building nonlinearalgorithms to minimize loss functions(a crucial ingredient in all optimizationproblems).2Training of any ML

10、 forecasting model requires the assessment stage.This stageforesees comparison of predicted and actual results.It brings an understandingof how well the model performs.After that,it would be possible to comparedifferent forecasting algorithms and choose the one which produces a minimalamount of erro

11、rs.With this approach,businesses can replace traditionaltechniques with ML,getting the following benefits for their business forecast:Acquiring insights and detecting hidden patterns that are difficult to tracewith traditional approaches.Training ML forecasting models on BigData,and moving computati

12、on to Cloud is becoming de-facto an industrystandard.Reduced number of errors in forecasting.For instance,McKinsey claimsthat AI-driven forecasting models applied to delivery chain managementcan reduce the number of errors by 2050%.Ability to infuse more data in a model.External data may be valuable

13、 hereand change the outcomes in terms of predictions.Flexibility and rapid adaptability to changes.Compared to traditionalnon-AI approaches,ML forecasting algorithms can be quickly adapted incase of any significant changes.Please note that were considering forecasting,not predictive modeling.Wellexp

14、lain the difference between these two models in simple terms.Difference Between Forecasting&PredictiveModelingBoth forecasting and predictive algorithms are applied to address cumbersomechallenges related to business planning,customer behavior,anddecision-making.But,nevertheless,these techniques dif

15、fer.Forecasting modeling implies analysis of past and present data to find patterns,or trends,which allow us to estimate the probability of future events.In contrastto predicting,forecasting modeling should have traceable logics.Typical usecases include a forecast for energy consumption in the follo

16、wing 612 months,an evaluation of how many customers will reach support in the next 7 days,or3how many agreements for the supply are expected to be signed.All this could beforecasted based on previous(historical)data.Predictive modeling is the process of applying AI and data mining to assess moredeta

17、iled,specific outcomes and use much more diverse data types.Thedifference between predictive and forecasting modeling is blurred,still,we canconsider an example to understand it better.Just imagine that a creditinstitution plans to launch a new premium card.At this point,two questions mayarise.The f

18、irst will probably be,how many cards will be issued in the next 6 months?Forecasting modeling will help us find an answer to this question thanks toanalysis of similar products launched in the past.But we still dont know whomwe can recommend this card to.Here predictive modeling comes into play.Iten

19、ables us to analyze a customer information database with such fields as age,salary,preferences,consumer habits,etc.With this approach,we will eventuallyunderstand which clients are more likely to use this card.Use ases For Machine Learning ForecastingFor BusinessFINANCIAL FORECASTINGWithout a financ

20、ial forecast,companies face disruption in processes andperformance,while C-level managers tend to make incorrect decisions.Thatswhy companies leverage ML forecasting which instead of dealing with mundanetasks,concentrates attention on understanding business drivers.Moreover,MLfinancial forecasting r

21、educes the amount of ineffective strategies in play andhuman errors and helps predict supply,demand,inventory,future revenues,expenses,and cash flow.For example,stakeholders of the business are aiming to know the companysturnover and key factors for growth during the next financial period toundersta

22、nd and analyze areas of improvement.Based on historical key companybusiness indicators and existing turnover information during the past periods,we can develop an ML forecasting model using deep learning or regressionmodels.It will predict future required metrics,based also on seasonal4information a

23、nd other influencing factors.In this case,business owners will beable to plan the next period of time accordingly.SUPPLY CHAIN FORECASTINGML can fully transform management in the area of supply chains,which arebecoming more globalized and sophisticated.ML-based forecasting solutionsenable companies

24、to efficiently respond to issues and threats as well as avoidunder and overstocking.Machine learning algorithms for forecasting can learnrelationships from a training dataset and then apply these relationships to newdata.Thus,ML improves selecting and segmenting suppliers,predicting supplychain risk

25、s,inventory management,and transportation and distributionprocesses.Lets look at an example of using machine learning for supply chain forecasting.The chain of hypermarkets operates around 100 stores in different locations andhas an average of 50000 SKUs per store.For such a big chain,its definitely

26、required that the process of replenishment of warehouses be automated.Thereare two main benefits in this case:1.No need to store a lot of hard-to-sell products2.Frequently sold products should be delivered on timeBased on the previous information on replenishment of warehouses,as well asdata that sh

27、ows how fast certain products are selling,we can develop an MLmodel for predicting the number of products per SKU.The prediction could beshown with different time horizons(e.g.daily,weekly,monthly,etc.).This canhelp managers properly organize the system of storing products and minimizethe case of pr

28、oduct absence.5PRICE PREDICTIONPrice prediction algorithms determine how much the product must cost to beappealing to consumers,meet the companys expectations,and assure thehighest level of sales.The construction of price forecasts should take intoaccount such factors as product features,demand,and

29、existing trends.Thisapproach may be perceived skeptically,yet its beneficial when companies entera new market or release a new product and want to easily cope with a myriad offluctuating factors.Often business owners want to have an understanding of price changes for aspecific product for a future p

30、eriod of time.Having taken into considerationclient data with related price changes for a past period of time for all of theexisting products,we can catch general patterns from the previous data andextrapolate them for the next periods.The positive impact could also be appliedby adding external thir

31、d-party data that could influence prices as well,forinstance:inflation rate,holidays,seasonal patterns,etc.Wrapping up all of thisdata,we can develop an ML forecasting model that will be able to predict pricetrends for specific products.DEMAND&SALES FORECASTINGA fluctuation in demand is a cumbersome

32、 challenge that concerns the wholee-commerce industry.Thats why companies,including manufacturers,apply MLdemand forecasting to predict buyers behavior and find out how many productsto produce or order.With ML models,its possible to avoid excess inventory orstockout.Moreover,such an approach to dema

33、nd forecasting enablesunderstanding the target audience and competition.Lets say a restaurant chain business wants to plan demand in advance.It willhelp the business in several ways:to know the number of dishes that will be sold in the restaurant in orderto plan food stock in advance,to understand a

34、nd define an appropriate number of employees that arerequired to provide quality customer serviceto come up with the proper and timely marketing campaign6In order to develop a demand forecasting model and help businesses to fulfilltheir goals,it will be great to start by analyzing historical data of

35、 the previousperiods.One of the ways to improve the model performance could be anintegration of NLP algorithms as well.For example,we can consider reviews onGoogle for our restaurant chain,as well as the main competitors to identify themain dishes/quality of service that customers like or do not lik

36、e.FRAUD DETECTIONAccording to a TransUnion report,there is a 52.2%increase in the rate ofsuspected digital fraud globally between 2019 and 2021.It indicates thatcompanies should make greater efforts in the development of anti-fraud tactics.ML algorithms can detect suspicious financial transactions b

37、y learning from pastdata.They are already successfully applied in e-commerce,banking,healthcare,fintech,and other areas.For instance,a cafe chain owner wants to analyze the productivity of employees.One of the main goals is to detect hidden patterns that allow employees tocheat.Different frauds like

38、 this could lead to losing money.Based on historicaldata,we can develop a fraud detection model that will detect anomaly patternsand notify about them.In this case,managers can precisely analyze detectedanomalies and identify the root cause of such deviations in the data.In thefuture,such cases coul

39、d be prevented by the manager to keep the businesssafe.7Key Machine Learning ForecastingAlgorithmsLets look at some key machine learning forecasting algorithms to betterunderstand how ML forecasting can be applied.REGRESSION ALGORITHMSML regression models are applied to predict trends and outcomes,b

40、eingcapable of comprehending how variables impact each other along with theresults.The dependency between variables can be both linear and nonlinear,while labeled data is required for training.After understanding the relationshipof variables,regression models can predict what results will be in unse

41、en data.Simple and multiple linear regression and logistic regression,where a targetvariable has only two values,are one of the most common baseline models topredict sales,stock prices,and customer behavior.DEEP LEARNING ALGORITHMSTime series forecasting implementation is gradually replenishing with

42、 new deeplearning algorithms.The more versatile and explainable a model is,the higherthe chances for its production use.Lets take a look at a few deep learningmodels for time series forecasting.The first one is DeepAR.Its a supervised ML algorithm created by Amazon andbased on recurrent neural netwo

43、rks.It has proven its efficiency with datasetsconsisting of hundreds of interrelated time series.The advantages of themethod are the possibility to use a rich set of inputs,scaling capabilities,andsuitability for probabilistic forecasting.The second one is the Temporal Fusion Transformer(TFT).It ove

44、rcomes otherdeep learning models in terms of versatility and can be built on multiple timeseries.TFT performs well even if trained on a small dataset,thus being suitablefor demand forecasting as just one example.The third algorithm is long short-term memory(LSTM)based upon an artificialRNN,in which

45、the output from one step is transformed into the input of the next8step.As for the architecture of LSTM,it consists of neural networks and memorycells for maintaining data,while any manipulation within the memory isperformed by gates.There are three gates here:Forget,Input,and Output.However,LSTM re

46、quires plenty of resources and a long time for training.TREE-BASED ALGORITHMSTree-based algorithms refer to supervised learning approaches.Theiradvantages include accuracy,sustainability,and suitability for mappingnon-linear patterns.The idea here is to define homogeneous sets in the sampletaking in

47、to account the key differentiator in input.The classification oftree-based algorithms depends on the target variable.As for advantages,tree-based algorithms can be easily grasped,require minimal data cleaning,andhandle different types of variables.The tendency toward overfitting andirreconcilability

48、 with continuous variables may be seen as disadvantages in thiscase.GAUSSIAN PROCESSESGaussian processes(GP)are inferior in popularity to other models,yet they arepowerful enough for industrial application,including automatic forecasting.Gaussian processes enable us to incorporate expert opinion via

49、 kernel,thoughtheir application in forecasting depends on the number of parameters and maybe expensive.AUTO-REGRESSIVE ALGORITHMSThe group of auto-regression algorithms foresees predicting future values usingthe output from the previous step as an input.Forecasting algorithms of thisgroup include AR

50、IMA,SARIMA,and others.In ARIMA,forecasting is carried outwith the application of moving and autoregressive averages.For instance,theARIMA model can predict fuel costs or forecast a companys revenue based onpast periods.SARIMA uses the same basic idea,but it includes a seasonalcomponent that may affe

51、ct the outcomes.EXPONENTIAL SMOOTHINGExponential smoothing is an alternative to ARIMA models.It can be applied as aforecasting model for univariate data that can be extended to support data with9a systematic trend or seasonal component.In this model,forecasting is aweighted sum of past observations,

52、yet the importance(weight)of pastobservations is exponentially decreased.The accuracy of prediction depends onthe type of the exponential smoothing model which can be single,double,ortriple.The most sophisticated exponential smoothing models take into accounttrends and seasonality.How to Apply Machi

53、ne Learning ForecastingRegardless of the chosen model,the whole adoption of ML practices looks as thefollowing:1.Define business goals and available internal data2.Search for external data,namely market reports,trends,GDPs,productreviews,etc.3.Structure,clean,and label data(if needed)4.Identify the

54、batch of problems to be solved with the help of forecasting5.Select a baseline model(usually simple regression or tree-based models)to be used as a first reference point to start with6.Improve models performance by implementing more sophisticated MLmodels or adjusting the data7.After achieving comfo

55、rtable results,the model is implemented intoproduction(added to existing software and used on more data)Challenges of ML ForecastingNothing good comes without challenges,ML forecasting is no exception.Keybusiness forecasting with machine learning challenges include the following:Insufficient amount

56、of data to train a model10An incorrectly chosen metric to evaluate results in alignment withbusiness needsImputation of missing dataDealing with outliers/anomaliesWhile infusing the data at the scale of AI,businesses encounter difficulties andlimitations,thats why its crucial to involve experienced

57、data scienceprofessionals and AI engineers when implementing machine learning.The MobiDev team helps businesses to address challenges and implementforecasting using machine learning to yield new insights.Years of experienceand successful implementation of more than 450 projects allows our experts to

58、build effective solutions for enhancing business processes.We focus on thegoals of each specific project to create a roadmap that will help our clientsachieve measurable results.Feel free to contact us if you need help with the implementation of machinelearning forecasting or other AI-based features.The MobiDev experts will dotheir best to help you.11