Comparative analysis of short-term demand predicting models using ARIMA and deep learning

The forecasting consists of taking historical data as inputs then using them to predict future observations, thus determining future trends. Demand prediction is a crucial component in the supply chain’s process that allows each member to enhance its performance and its profit. Nevertheless, because of demand uncertainty supply chains usually suffer from many problems such as the bullwhip effect. As a solution to those logistics issues, this paper presents a comparative analysis of four time series demand forecasting models; namely, the autoregressive integrated moving Average (ARIMA) a statistical model, the multi-layer perceptron (MLP) a feedforward neural network, the long short-term memory model (LSTM) a recurrent neural network and the convolutional neural network (CNN or ConvNet) a deep learning model. The experimentations are carried out using a real-life dataset provided by a supermarket in Morocco. The results clearly show that the convolutional neural network gives slightly better forecasting results than the Long short-term memory network

Software development metrics prediction using time series methods

The software development process is an intricate task, with the growing complexity of software solutions and inflating code-line count being part of the reason for the fall of software code coherence and readability thus being one of the causes for software faults and it’s declining quality. Debugging software during development is significantly less expensive than attempting damage control after the software’s release. An automated quality-related analysis of developed code, which includes code analysis and correlation of development data like an ideal solution. In this paper the ability to predict software faults and software quality is scrutinized. Hereby we investigate four models that can be used to analyze time-based data series for prediction of trends observed in the software development process are investigated. Those models are Exponential Smoothing, the Holt-Winters Model, Autoregressive Integrated Moving Average (ARIMA) and Recurrent Neural Networks (RNN). Time-series analysis methods prove a good fit for software related data prediction. Such methods and tools can lend a helping hand for Product Owners in their daily decision-making process as related to e.g. assignment of tasks, time predictions, bugs predictions, time to release etc. Results of the research are presented.Peer ReviewedPostprint (author's final draft

Web Traffic Time Series Forecasting

Online web traffic forecasting is one of the most crucial elements of maintaining and improving websites and digital platforms. Traffic patterns usually predict future online traffic, including page views, unique visitors, session duration, and bounce rates. However, it is challenging to forecast non-stationary online web traffic, particularly when the data has spikes or irregular patterns. This non-stationary property demands a more advanced forecasting technique. In this study, we provide a neural networkbased method, Spiking Neural Networks (SNNs), for dealing with the data spikes and irregular patterns in non-stationary data. In our study, we compared the forecasting results of SNNs with traditional and popular time-series prediction methods like Long Short-Term Memory (LSTM) networks and Seasonal AutoRegressive Integrated Moving Average with exogenous variables (SARIMAX). The evaluation was based on prediction error metrics such as Mean Square Error (RMSE) and the Mean Absolute Error (MAE). Our results found that SNNs worked better in forecasting the non-stationary web traffic data when compared to the traditional methods. This effective forecasting technique by SNNs can be crucial in sectors like e-commerce and digital marketing, where accurately predicting the traffic helps optimize resources and improve digital strategies

Hybrid Methods for Time Series Forecasting

Time series forecasting is a crucial task in various fields of business and science. There are two coexisting approaches to time series forecasting, which are statistical methods and machine learning methods. Both come with different strengths and limitations. Statistical methods such as the Holt-Winters’ Method or ARIMA have been practiced for decades. They stand out due to their robustness and flexibility. Furthermore, these methods work well when few data is available and can exploit a priori knowledge. However, statistical methods assume linear relationships in the data, which is not necessarily the case in real-world data, inhibiting forecasting performance. On the other hand, machine learning methods such as Multilayer Perceptrons or Long Short-Term Memory Networks do not have the assumption of linearity and have the exceptional advantage of universally approximating almost any function. In addition to that, machine learning methods can exploit cross-series information to enhance an individual forecast. Besides these strengths, machine learning methods face several limitations in terms of data and computation requirements. Hybrid methods promise to advance time series forecasting by combining the best of statistical and machine learning methods. The fundamental idea is that the combination compensates for the limitations of one approach with the strengths of the other. This thesis shows that the combination of a Holt-Winters’ Method and a Long Short-Term Memory Network is promising when the periodicity of a time series can be precisely specified. The precise specification enables the Holt-Winters’ Method to simplify the forecasting task for the Long Short-Term Memory Network and, consequently, facilitates the hybrid method to obtain accurate forecasts. The research question to be answered is which characteristics of a time series determine the superiority of either statistical, machine learning, or hybrid approaches. The result of the conducted experiment shows that this research question can not be answered generally. Nevertheless, the results propose findings for specific forecasting methods. The Holt-Winters’ Method provides reliable forecasts when the periodicity can be precisely determined. ARIMA, however, handles overlying seasonalities better than the Holt-Winters’ Method due to its autoregressive approach. Furthermore, the results suggest the hypothesis that machine learning methods have difficulties extrapolating time series with trend. Finally, the Multilayer Perceptron can conduct accurate forecasts for various time series despite its simplicity, and the Long Short-Term Memory Network proves that it needs relevant datasets of adequate length to conduct accurate forecasts

A COMPARISION USING STATISTICAL AND MACHINE LEARNING METHODS FOR STREAMFLOW TIME SERIES

This study was carried out in the Sibinacocha lake watershed in the Peruvian Andes. In this region the long-term meteorological data are scarce and there are few studies of flow forecasts. Based on this evidence, in this study we present the monthly flow simulation, using statistical models and data-oriented model, with the purpose of evaluating the performance of these methodologies. The results of the comparative statistical analyses indicated that the data-oriented models, specifically the Recurrent Neural Networks, provided great improvements over the other models applied, specifically the ability to capture the minimum and maximum monthly flow, resulting in excellent statistical values (R2=0.85, d=0.96), thus suggesting this methodology as a possible application for flow forecasts

Using Long-Short-Term-Memory Recurrent Neural Networks To Predict Aviation Engine Vibrations

This thesis examines building viable Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) neurons to predict aircraft engine vibrations. The different networks are trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, and this database contains multiple types of engines. Further, LSTM RNNs provide a “memory” of the contribution of previous time series data which can further improve predictions of future vibration values. LSTM RNNs were used over traditional RNNs, as those suffer from vanishing/exploding gradients when trained with back propagation. The study managed to predict vibration values for 1, 5, 10, and 20 seconds in the future, with 2.84% 3.3%, 5.51% and 10.19% mean absolute error, respectively. These neural networks provide a promising means for the future development of warning systems so that suitable actions can be taken before the occurrence of excess vibration to avoid unfavorable situations during flight