Forecasting third-party mobile payments with implications for customer flow prediction

Ma, Shaohui; Fildes, Robert

doi:10.1016/j.ijforecast.2019.08.012

Cited by 32 publications

(11 citation statements)

References 60 publications

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“…Our work decouples the complexity from a particular model, and therefore motivates and justifies the use of alternative model classes (e.g. trees in [37], featurization, kernel methods or nearest neighbors). The justification increases the "toolbox" available to practitioners.…”

Section: Modern Practices and Model Classes For Global Methodsmentioning

confidence: 78%

Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality

Montero-Manso¹,

Hyndman²

2020

Preprint

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Forecasting groups of time series is of increasing practical importance. Some examples are: forecasting the demand for multiple products offered by a retailer, server loads within a data center or the number of completed ride shares in zones within a city. The local approach to this problem considers each time series separately and fits a function or model to each series. The global approach considers all time series as the same regression task and fits a single function to all series. For groups of similar time series, global methods outperform the more established local methods. However, there is recent empirical evidence showing surprisingly good performance of global models on heterogeneous groups of time series. This suggests a more general applicability of global methods, with major implications in forecasting theory and practice, in the form of more accurate tools for automatic forecasting and new scenarios to study. However, the evidence has been of empirical nature and a more fundamental study is required. In this paper, we formalize the setting of forecasting a set of time series with local and global learning algorithms, leading to the following contributions:• We show that global methods are not more restrictive than local methods for time series forecasting, a result which does not apply to sets of regression problems in general. Global and local methods can produce the same forecasts without any assumptions about similarity of the series in the set. This result shows that global models can succeed in a wider range of problems than previously thought. • We derive basic generalization bounds for local and global algorithms. We find that the complexity of local methods grows with the size of the set while it remains constant for global methods. Therefore a global algorithm can afford to be quite complex and still benefit from better generalization error than local methods for large datasets. These bounds serve to clarify and support recent experimental results in the area of time series forecasting, and guide the design of new algorithms. For the specific class of limited-memory autoregressive models, this bound leads to the design of global models with much larger memory than what is effective for local methods. • The findings are supported by an extensive empirical study. We show that purposely naïve algorithms derived from these principles, such as global linear models fit by least squares, deep networks or even high order polynomials, result in superior accuracy in benchmark datasets. In particular, global linear models show an unreasonable effectiveness, providing competitive forecasting accuracy with far fewer parameters than the simplest of local methods. Empirical evidence points towards global models being able to automatically learn long memory patterns and related effects that are only available to local models if introduced manually. Keywords Time SeriesConsider the problem of having to forecast many time series as a group. We might need to forecast tourist arrivals at all our resorts for n...

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Section: Modern Practices and Model Classes For Global Methodsmentioning

confidence: 78%

Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality

Montero-Manso¹,

Hyndman²

2020

Preprint

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“…Due to separated models are used to forecasting two points, there is no opportunity of handling the dependency between two consecutive predictions. According to Ma and Fildes [66] , comparative studies between Recursive and Direct strategies have shown contradictory results as to which strategy achieves better forecasting performances than others. While Rana and Rahman [67] achieved better results for direct method over the recursive method, Xue et al [68] provided evidence of better results from the recursive method over the direct method.…”

Section: Methodsmentioning

confidence: 99%

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods

Ribeiro

Mariani

Coelho

2020

Journal of Biomedical Informatics

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Epidemiological time series forecasting plays an important role in health public systems, due to its ability to allow managers to develop strategic planning to avoid possible epidemics. In this paper, a hybrid learning framework is developed to forecast multi-step-ahead (one, two, and three-month-ahead) meningitis cases in four states of Brazil. First, the proposed approach applies an ensemble empirical mode decomposition (EEMD) to decompose the data into intrinsic mode functions and residual components. Then, each component is used as the input of five different forecasting models, and, from there, forecasted results are obtained. Finally, all combinations of models and components are developed, and for each case, the forecasted results are weighted integrated (WI) to formulate a heterogeneous ensemble forecaster for the monthly meningitis cases. In the final stage, a multi-objective optimization (MOO) using the Non-Dominated Sorting Genetic Algorithm – version II is employed to find a set of candidates’ weights, and then the Technique for Order of Preference by similarity to Ideal Solution (TOPSIS) is applied to choose the adequate set of weights. Next, the most adequate model is the one with the best generalization capacity out-of-sample in terms of performance criteria including mean absolute error (MAE), relative root mean squared error (RRMSE), and symmetric mean absolute percentage error (sMAPE). By using MOO, the intention is to enhance the performance of the forecasting models by improving simultaneously their accuracy and stability measures. To access the model’s performance, comparisons based on metrics are conducted with: (i) EEMD, heterogeneous ensemble integrated by direct strategy, or simple sum; (ii) EEMD, homogeneous ensemble of components WI; (iii) models without signal decomposition. At this stage, MAE, RRMSE, and sMAPE criteria as well as Diebold–Mariano statistical test are adopted. In all twelve scenarios, the proposed framework was able to perform more accurate and stable forecasts, which showed, on 89.17% of the cases, that the errors of the proposed approach are statistically lower than other approaches. These results showed that combining EEMD, heterogeneous ensemble and WI with weights obtained by optimization can develop precise and stable forecasts. The modeling developed in this paper is promising and can be used by managers to support decision making.

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“…Peramalan memungkinkan membantu perencanaan inventaris, harga kompetitif, dan strategi promosi tepat waktu [16], [17]. Peramalan penjualan dapat membantu proses memahami siklus hidup bisnis dalam hal penjualan dan dampak sasaran produk jangka pendek seperti pertumbuhan, stabilitas, penurunan, promosi maupun harga [18]. Menurut penelitian yang dilakukan oleh [17], mereka mengusulkan pendekatan Convolutional Neural Network (CNN) untuk melakukan peramalan penjualan di e-commerce.…”

Section: Tinjauan Literaturunclassified

Analisis Prediktif untuk Keputusan Bisnis : Peramalan Penjualan

Husein

Lubis

Harahap³

2021

dsi

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Peramalan penjualan produk adalah aspek utama dari manajemen pembelian, persediaan yang melebihi permintaan atau kekurangan akan berdampak pada manajemen pelayanan maupun secara ekominis. Makalah ini fokus mencoba menyajikan penerapan analisis prediktif dengan mengadopsi kerangka kerja Data Science (ilmu data) untuk menemukan wawasan yang berguna dalam pengambilan keputusan bisnis khususnya tentang peramalan penjualan produk di masa depan. Kerangka CRISP-DM diusulkan dengan tahapan pemahasan bisnis, pemahaman dan persiapan data, exploratory data analysis (EDA) dan pemodelan. Berdasarkan hasil pengujian data penjualan yang dievaluasi berdasarkan RMSE dan MAE, algoritma XGBoost menghasilkan prediksi berada dalam 1,3% kemudian ARIMA sebesar 1.6%, masih lebih baik dibandingkan LinearRegression, RandomForestdan LSTM dengan tingkat kesalahan sebesar 1.81%, 1.97%, 2.21% pada masing-masing algoritma dari data aktual.

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Forecasting third-party mobile payments with implications for customer flow prediction

Cited by 32 publications

References 60 publications

Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality

Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods

Analisis Prediktif untuk Keputusan Bisnis : Peramalan Penjualan

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