Quantitative market and sales forecast accuracy analysis

The main objective of this master’s thesis was to quantitatively study the reliability of market and sales forecasts of a certain company by measuring bias, precision and accuracy of these forecasts by comparing forecasts against actual values. Secondly, the differences of bias, precision and accuracy between markets were explained by various macroeconomic variables and market characteristics. Accuracy and precision of the forecasts seems to vary significantly depending on the market that is being forecasted, the variable that is being forecasted, the estimation period, the length of the estimated period, the forecast horizon and the granularity of the data. High inflation, low income level and high year-on-year market volatility seems to be related with higher annual market forecast uncertainty and high year-on-year sales volatility with higher sales forecast uncertainty. When quarterly market size is forecasted, correlation between macroeconomic variables and forecast errors reduces. Uncertainty of the sales forecasts cannot be explained with macroeconomic variables. Longer forecasts are more uncertain, shorter estimated period leads to higher uncertainty, and usually more recent market forecasts are less uncertain. Sales forecasts seem to be more uncertain than market forecasts, because they incorporate both market size and market share risks. When lead time is more than one year, forecast risk seems to grow as a function of root forecast horizon. When lead time is less than year, sequential error terms are typically correlated, and therefore forecast errors are trending or mean-reverting. The bias of forecasts seems to change in cycles, and therefore the future forecasts cannot be systematically adjusted with it. The MASE cannot be used to measure whether the forecast can anticipate year-on-year volatility. Instead, we constructed a new relative accuracy measure to cope with this particular situation.

Framework for Traffic Pattern Identification: Required Step for Short-term Forecasting

In the world of transport management, the term ‘anticipation’ is gradually replacing ‘reaction’. Indeed, the ability to forecast traffic evolution in a network should ideally form the basis for many traffic management strategies and multiple ITS applications. Real-time prediction capabilities are therefore becoming a concrete need for the management of networks, both for urban and interurban environments, and today’s road operator has increasingly complex and exacting requirements. Recognising temporal patterns in traffic or the manner in which sequential traffic events evolve over time have been important considerations in short-term traffic forecasting. However, little work has been conducted in the area of identifying or associating traffic pattern occurrence with prevailing traffic conditions. This paper presents a framework for detection pattern identification based on finite mixture models using the EM algorithm for parameter estimation. The computation results have been conducted taking into account the traffic data available in an urban network.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Improving demand forecasting practices in the industrial context

Demand forecasting is one of the fundamental managerial tasks. Most companies do not know their future demands, so they have to make plans based on demand forecasts. The literature offers many methods and approaches for producing forecasts. When selecting the forecasting approach, companies need to estimate the benefits provided by particular methods, as well as the resources that applying the methods call for. Former literature points out that even though many forecasting methods are available, selecting a suitable approach and implementing and managing it is a complex cross-functional matter. However, research that focuses on the managerial side of forecasting is relatively rare. This thesis explores the managerial problems that are involved when demand forecasting methods are applied in a context where a company produces products for other manufacturing companies. Industrial companies have some characteristics that differ from consumer companies, e.g. typically a lower number of customers and closer relationships with customers than in consumer companies. The research questions of this thesis are: 1. What kind of challenges are there in organizing an adequate forecasting process in the industrial context? 2. What kind of tools of analysis can be utilized to support the improvement of the forecasting process? The main methodological approach in this study is design science, where the main objective is to develop tentative solutions to real-life problems. The research data has been collected from two organizations. Managerial problems in organizing demand forecasting can be found in four interlinked areas: 1. defining the operational environment for forecasting, 2. defining the forecasting methods, 3. defining the organizational responsibilities, and 4. defining the forecasting performance measurement process. In all these areas, examples of managerial problems are described, and approaches for mitigating these problems are outlined.

Weather based warning systems for bean angular-leaf-spot and anthracnose

Data available in the literature were used to develop a warning system for bean angular leaf spot and anthracnose, caused by Phaeoisariopsis griseola and Colletotrichum lindemuthianum, respectively. The model is based on favorable environmental conditions for the infectious process such as continuous leaf wetness duration and mean air temperature during this subphase of the pathogen-host relationship cycle. Equations published by DALLA PRIA (1977) showing the interactions of those two factors on the disease severity were used. Excell spreadsheet was used to calculate the leaf wetness period needed to cause different infection probabilities at different temperature ranges. These data were employed to elaborate critical period tables used to program a computerized electronic device that records leaf wetness duration and mean temperature and automatically shows the daily disease severity value (DDSV) for each disease. The model should be validated in field experiments under natural infection for which the daily disease severity sum (DDSS) should be identified as a criterion to indicate the beginning and the interval of fungicide applications to control both diseases.

Designing a Demand Forecasting Process in the Fast Moving Consumer Goods Context

The purpose of this thesis was to study the design of demand forecasting processes. A literature review in the field of forecasting was conducted, including general forecasting process design, forecasting methods and techniques, the role of human judgment in forecasting and forecasting performance measurement. The purpose of the literature review was to identify the important design choices that an organization aiming to design or re-design their demand forecasting process would have to make. In the empirical part of the study, these choices and the existing knowledge behind them was assessed in a case study where a demand forecasting process was re-designed for a company in the fast moving consumer goods business. The new target process is described, as well as the reasoning behind the design choices made during the re-design process. As a result, the most important design choices are highlighted, as well as their immediate effect on other processes directly tied to the demand forecasting process. Additionally, some new insights on the organizational aspects of demand forecasting processes are explored. The preliminary results indicate that in this case the new process did improve forecasting accuracy, although organizational issues related to the process proved to be more challenging than anticipated.

QT interval in healthy dogs: which method of correcting the QT interval in dogs is appropriate for use in small animal clinics?

The electrocardiography (ECG) QT interval is influenced by fluctuations in heart rate (HR) what may lead to misinterpretation of its length. Considering that alterations in QT interval length reflect abnormalities of the ventricular repolarisation which predispose to occurrence of arrhythmias, this variable must be properly evaluated. The aim of this work is to determine which method of correcting the QT interval is the most appropriate for dogs regarding different ranges of normal HR (different breeds). Healthy adult dogs (n=130; German Shepherd, Boxer, Pit Bull Terrier, and Poodle) were submitted to ECG examination and QT intervals were determined in triplicates from the bipolar limb II lead and corrected for the effects of HR through the application of three published formulae involving quadratic, cubic or linear regression. The mean corrected QT values (QTc) obtained using the diverse formulae were significantly different (ρ<0.05), while those derived according to the equation QTcV = QT + 0.087(1- RR) were the most consistent (linear regression). QTcV values were strongly correlated (r=0.83) with the QT interval and showed a coefficient of variation of 8.37% and a 95% confidence interval of 0.22-0.23 s. Owing to its simplicity and reliability, the QTcV was considered the most appropriate to be used for the correction of QT interval in dogs.

Price spike forecasting in a competitive day-ahead energy market

Electricity price forecasting has become an important area of research in the aftermath of the worldwide deregulation of the power industry that launched competitive electricity markets now embracing all market participants including generation and retail companies, transmission network providers, and market managers. Based on the needs of the market, a variety of approaches forecasting day-ahead electricity prices have been proposed over the last decades. However, most of the existing approaches are reasonably effective for normal range prices but disregard price spike events, which are caused by a number of complex factors and occur during periods of market stress. In the early research, price spikes were truncated before application of the forecasting model to reduce the influence of such observations on the estimation of the model parameters; otherwise, a very large forecast error would be generated on price spike occasions. Electricity price spikes, however, are significant for energy market participants to stay competitive in a market. Accurate price spike forecasting is important for generation companies to strategically bid into the market and to optimally manage their assets; for retailer companies, since they cannot pass the spikes onto final customers, and finally, for market managers to provide better management and planning for the energy market. This doctoral thesis aims at deriving a methodology able to accurately predict not only the day-ahead electricity prices within the normal range but also the price spikes. The Finnish day-ahead energy market of Nord Pool Spot is selected as the case market, and its structure is studied in detail. It is almost universally agreed in the forecasting literature that no single method is best in every situation. Since the real-world problems are often complex in nature, no single model is able to capture different patterns equally well. Therefore, a hybrid methodology that enhances the modeling capabilities appears to be a possibly productive strategy for practical use when electricity prices are predicted. The price forecasting methodology is proposed through a hybrid model applied to the price forecasting in the Finnish day-ahead energy market. The iterative search procedure employed within the methodology is developed to tune the model parameters and select the optimal input set of the explanatory variables. The numerical studies show that the proposed methodology has more accurate behavior than all other examined methods most recently applied to case studies of energy markets in different countries. The obtained results can be considered as providing extensive and useful information for participants of the day-ahead energy market, who have limited and uncertain information for price prediction to set up an optimal short-term operation portfolio. Although the focus of this work is primarily on the Finnish price area of Nord Pool Spot, given the result of this work, it is very likely that the same methodology will give good results when forecasting the prices on energy markets of other countries.

Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

Demand forecasting process design and methods in medium sized enterprise

The purpose of this thesis was to study the design of demand forecasting processes and management of demand. In literature review were different processes found and forecasting methods and techniques interviewed. Also role of bullwhip effect in supply chain was identified and how to manage it with information sharing operations. In the empirical part of study is at first described current situation and challenges in case company. After that will new way to handle demand introduced with target budget creation and how information sharing with 5 products and a few customers would bring benefits to company. Also the new S&OP process created within this study and organization for it.

Cholinergic stimulation with pyridostigmine reduces the QTc interval in coronary artery disease

Parasympathetic dysfunction is an independent risk factor in patients with coronary artery disease; thus, cholinergic stimulation is a potential therapeutic measure that may be protective by acting on ventricular repolarization. The purpose of the present study was to determine the effects of pyridostigmine bromide (PYR), a reversible anticholinesterase agent, on the electrocardiographic variables, particularly QTc interval, in patients with stable coronary artery disease. In a randomized double-blind crossover placebo-controlled study, simultaneous 12-lead electrocardiographic tracings were obtained at rest from 10 patients with exercise-induced myocardial ischemia before and 2 h after the oral administration of 45 mg PYR or placebo. PYR increased the RR intervals (pre: 921 ± 27 ms vs post: 1127 ± 37 ms; P<0.01) and, in contrast with placebo, decreased the QTc interval (pre: 401 ± 3 ms vs post: 382 ± 3 ms; P<0.01). No other electrocardiographic variables were modified (PR segment, QT interval, QT and QTc dispersions). Cholinergic stimulation with PYR caused bradycardia and reduced the QTc interval without important side effects in patients with coronary disease. These effects, if confirmed in studies over longer periods of administration, may suggest a cardioprotection by cholinergic stimulation with PYR.

Chronic converting enzyme inhibition normalizes QT interval in aging rats

The aim of the present study was to investigate the effects of converting enzyme inhibition by captopril on ECG parameters in aged rats. Four-month-old male rats received captopril dissolved in tap water (0.5 mg/l) or tap water for 2 or 20 months. At the end of treatment, under anesthesia, RR and PR interval, P wave and QRS duration, QT and corrected QT interval were measured in all animals. On the following day, chronic ECG (lead II) recordings were performed to quantify supraventricular (SVPB) or ventricular premature beats (VPB). After sacrifice, the hearts were removed and weighed. RR interval was similar in young and untreated aged rats, but significantly larger in aged rats treated with captopril. P wave and QRS length did not differ among groups. PR interval was significantly larger in old than in young rats and was not affected by captopril. Corrected QT interval was larger in aged than in young rats (117 ± 4 vs 64 ± 6 ms, P<0.05) and was reduced by captopril (71 ± 6 ms, P<0.05). VPB were absent in young rats and highly frequent in untreated old animals (8.4 ± 3.0/30 min). Captopril significantly reduced VPB in old rats (0.3 ± 0.1/30 min, P<0.05). The cardiac hypertrophy found in untreated aged rats was prevented by captopril (3.44 ± 0.14 vs 3.07 ± 0.10 mg/g, P<0.05). The beneficial effects of angiotensin converting enzyme inhibition on the rat heart during the aging process are remarkable.

Forecasting financial weather - can we foresee market sentiment? Spectrum of stock price behavior - NYSE case study

The desire to create a statistical or mathematical model, which would allow predicting the future changes in stock prices, was born many years ago. Economists and mathematicians are trying to solve this task by applying statistical analysis and physical laws, but there are still no satisfactory results. The main reason for this is that a stock exchange is a non-stationary, unstable and complex system, which is influenced by many factors. In this thesis the New York Stock Exchange was considered as the system to be explored. A topological analysis, basic statistical tools and singular value decomposition were conducted for understanding the behavior of the market. Two methods for normalization of initial daily closure prices by Dow Jones and S&P500 were introduced and applied for further analysis. As a result, some unexpected features were identified, such as a shape of distribution of correlation matrix, a bulk of which is shifted to the right hand side with respect to zero. Also non-ergodicity of NYSE was confirmed graphically. It was shown, that singular vectors differ from each other by a constant factor. There are for certain results no clear conclusions from this work, but it creates a good basis for the further analysis of market topology.