Time series analysis and forecasting future values has been a major research focus since years ago Time series analysis and forecasting in time series data finds it significance in many applications such as business, stock market and exchange, weather, electricity demand, cost and usage of products such as fuels, electricity, etc and in any kind of place that has specific seasonal or trendy changes with time The forecasting of time series data provides the organization with useful information that is necessary for making important decisions In this paper, a detailed survey of the various techniques applied for forecasting different types of time series dataset is provided This survey covers the overall forecasting models, the algorithms used within the model and other optimization techniques used for better performance and accuracy The various performance evaluation parameters used for evaluating the forecasting models are also discussed in this paper This study gives the reader an idea about the various researches that take place within forecasting using the time series data

A survey on forecasting of time series data

In this article, we present a new model based on hybridization of fuzzy time series theory with artificial neural network (ANN). In fuzzy time series models, lengths of intervals always affect the results of forecasting. So, for creating the effective lengths of intervals of the historical time series data set, a new ''Re-Partitioning Discretization (RPD)'' approach is introduced in the proposed model. Many researchers suggest that high-order fuzzy relationships improve the forecasting accuracy of the models. Therefore, in this study, we use the high-order fuzzy relationships in order to obtain more accurate forecasting results. Most of the fuzzy time series models use the current state's fuzzified values to obtain the forecasting results. The utilization of current state's fuzzified values (right hand side fuzzy relations) for prediction degrades the predictive skill of the fuzzy time series models, because predicted values lie within the sample. Therefore, for advance forecasting of time series, previous state's fuzzified values (left hand side of fuzzy relations) are employed in the proposed model. To defuzzify these fuzzified time series values, an ANN based architecture is developed, and incorporated in the proposed model. The daily temperature data set of Taipei, China is used to evaluate the performance of the model. The proposed model is also validated by forecasting the stock exchange price in advance. The performance of the model is evaluated with various statistical parameters, which signify the efficiency of the model.

High-order fuzzy-neuro expert system for time series forecasting

Designing fuzzy time series forecasting models: A survey

Recently, there seems to be increased interest in time series forecasting using soft computing (SC) techniques, such as fuzzy sets, artificial neural networks (ANNs), rough set (RS) and evolutionary computing (EC). Among them, fuzzy set is widely used technique in this domain, which is referred to as “Fuzzy Time Series (FTS)”. In this survey, extensive information and knowledge are provided for the FTS concepts and their applications in time series forecasting. This article reviews and summarizes previous research works in the FTS modeling approach from the period 1993–2013 (June). Here, we also provide a brief introduction to SC techniques, because in many cases problems can be solved most effectively by integrating these techniques into different phases of the FTS modeling approach. Hence, several techniques that are hybridized with the FTS modeling approach are discussed briefly. We also identified various domains specific problems and research trends, and try to categorize them. The article ends with the implication for future works. This review may serve as a stepping stone for the amateurs and advanced researchers in this domain.

A brief review of modeling approaches based on fuzzy time series

Time series forecasting is one of the challenging problems for humankind. The traditional forecasting methods using mean regression models have severe shortcomings in reflecting real-world fluctuations. While new probabilistic methods rush to rescue, they fight with technical difficulties like quantile crossing or selecting a prior distribution. To meld the different strengths of these fields while avoiding their weaknesses, as well as, to push the boundary of the state-of-the-art, we introduce ForGAN - one step ahead probabilistic forecasting with generative adversarial networks. ForGAN utilizes the power of the conditional generative adversarial network to learn the data generating distribution and compute probabilistic forecasts from it. We argue how to evaluate ForGAN in opposition to regression methods. To investigate probabilistic forecasting of ForGAN, we create a new dataset and demonstrate our method abilities on it. This dataset will be made publicly available for comparison. Furthermore, we test ForGAN on two publicly available datasets, namely Mackey-Glass dataset and Internet traffic dataset (A5M), where the impressive performance of ForGAN demonstrate its high capability in forecasting future values.

Probabilistic Forecasting of Sensory Data With Generative Adversarial Networks – ForGAN

India is the second largest producer of fruits after China with on estimated production of 54.4 million tonnes in 2008. India’s diverse agro-climatic condition allows production of a wide range of tropical, subtropical and temperate fruits. Sighting huge business opportunities, retail giants like the UK’s Tesco, France’s Carrefour, and American Wal-Mart are looking to make a foray into the Indian retail market by providing the Indian customer a unique experience of shopping. Not lagging behind are the Indian cooperatives like Reliance with Reliance Fresh, Aditya Birla Group, ITC, Mahindra & Mahindra, and Adani Group.This research has specifically been carried out to understand the present organized agri-food retail sector in India as well as the objectives, strategies, and financial performances of six existing companies in this business. The research paper will enable business houses to plan and decide at what level they can venture the present highly fragmented Indian market.

Comparative Study of Organized Agri-Food Businesses in India:

In the era of uncertainty and chaos, the decision making is a complex process especially when it is related with the future prediction. The decision making process is fully dependent on the level of accuracy in forecasting. It is obvious that forecasting activities play a vital role in our daily life. The classical time series methods can not deal with forecasting problems in which, the values of time series are linguistic terms represented by fuzzy sets [1, 2]. Therefore, Song and Chissom [4] presented the theory of fuzzy time series to overcome the drawback of the classical time series methods. Time series prediction is a very important practical application with a diverse range of applications including economic and business planning, inventory and production control, weather forecasting, signal processing and control, Pattern matching etc. Based on the theory of fuzzy time series, Song presented some forecasting methods [4,5,6] to forecast the enrollments of the University of Alabama. Chen [8] presented a method to forecast the enrollments of the University of Alabama based on fuzzy time series. It has the advantage of reducing the calculation time and simplifying the calculation process. Hwang [10], used the differences of the enrollments to present a method to forecast the enrollments of the University of Alabama based on fuzzy time series. Huang extended Chen’s work presented in [9] and used simplified calculations with the addition of heuristic rules to forecast the enrollments. Chen [12] presented a forecasting method based on high order fuzzy time series for forecasting the enrollments of the University of Alabama. Chen [13] and Hwang presented a method based on fuzzy time series to forecast the temperature.

Fuzzy Time Series: A Realistic Method to Forecast Gross Domestic Capital of India

Retroperitoneal desmoid tumours are uncommon tumours usually occurring in young females in their reproductive age group and commonly have a benign course but sometimes they can become malignant. These tumours belong to family of myofibroblastic fibromatosis. These tumours are composed of fibroblastic cells with bands of collagen and ill defined cytoplasm and rarely show mitosis. Recurrence is common especially after incomplete excision. Retroperitoneal desmoid is an uncommon tumour among desmoid group which led to an obstructed labour. Here a 28-year-old female diagnosed to have a pelvic desmoid, was missed during the antenatal check-up, presented as an obstructed labour which necessitated a caesarean section. The patient underwent a definitive surgery three months later for excision of tumour. The tumour was excised through a combined abdominal and gluteal incision. Histopathological report revealed an aggressive fibromatosis (retroperitoneal desmoid).

An unusual presentation of aggressive fibromatosis (retroperitoneal desmoid tumour) in pelvic cavity presented as obstructed labour.

The world has seen the worst recession of the century. As per a study (2010), youth from the age group of 25–35 years is the second highest earning population of India. High disposable income and l...

Analysis of Investment Behaviour during Recovery Phase among Youth Investors of Indian Stock Market

Food industry of the world is expected to touch $992 billion in 2014 with a volume of over 586 billion transactions. The restaurant business in India has witnessed exponential growth over the last ten years and this could happen due to increasing disposable income of youth, changing life style, working couples and innovations in restaurant business models. The restaurant and hotel industry have evolved due to a variety of business models meeting the needs of all customer segments, and at the same time, developing standardising services and innovative menus. The objective of this paper is to examine a possible relationship between innovativeness, effectiveness and efficiency of a restaurant. For this study, data was collected from 175 restaurants. To conclude, this paper identifies important relationships between operational effectiveness, business performance and innovations in service delivery. This study also takes an overall perspective of innovation in the product (menu item), process, services and amenities and its impact on service effectiveness and will be useful for existing and new entrants in the restaurant business of India.

Mrinalini Shah

Papers

Comparative Study of Organized Agri-Food Businesses in India:

Fuzzy Time Series: A Realistic Method to Forecast Gross Domestic Capital of India

An unusual presentation of aggressive fibromatosis (retroperitoneal desmoid tumour) in pelvic cavity presented as obstructed labour.

Analysis of Investment Behaviour during Recovery Phase among Youth Investors of Indian Stock Market

Exploring effect of service innovation on business performance of restaurants in India