Browsing by Subject "forecasting"

Nowadays power consumption is a hot and actual domain. Efficient energy consumption allows you to use resources from the environment wisely and, moreover, switch to alternative energy sources where it is possible. This thesis is aimed at analyzing elevator’s power consumption data (average per every 5 minutes and 1 hour). The data has been gathered for several years, so it is a time series. This thesis includes review of time series models, which then can be used for the consequent analysis. Main directions are forecasting power consumption, capturing trends and anomalies. In addition, time series data may also be used for calculating average power consumption for each elevator inside the elevator group. As an outcome, spread of the power consumption across 4 elevators inside the elevator group may be seen. One of the thesis’ goals is to check whether it is even or not.

In recent years, thanks to developments in information technology, large-dimensional datasets have been increasingly available. Researchers now have access to thousands of economic series and the information contained in them can be used to create accurate forecasts and to test economic theories. To exploit this large amount of information, researchers and policymakers need an appropriate econometric model.Usual time series models, vector autoregression for example, cannot incorporate more than a few variables. There are two ways to solve this problem: use variable selection procedures or gather the information contained in the series to create an index model. This thesis focuses on one of the most widespread index model, the dynamic factor model (the theory behind this model, based on previous literature, is the core of the first part of this study), and its use in forecasting Finnish macroeconomic indicators (which is the focus of the second part of the thesis). In particular, I forecast economic activity indicators (e.g. GDP) and price indicators (e.g. consumer price index), from 3 large Finnish datasets. The first dataset contains a large series of aggregated data obtained from the Statistics Finland database. The second dataset is composed by economic indicators from Bank of Finland. The last dataset is formed by disaggregated data from Statistic Finland, which I call micro dataset. The forecasts are computed following a two steps procedure: in the first step I estimate a set of common factors from the original dataset. The second step consists in formulating forecasting equations including the factors extracted previously. The predictions are evaluated using relative mean squared forecast error, where the benchmark model is a univariate autoregressive model. The results are dataset-dependent. The forecasts based on factor models are very accurate for the first dataset (the Statistics Finland one), while they are considerably worse for the Bank of Finland dataset. The forecasts derived from the micro dataset are still good, but less accurate than the ones obtained in the first case. This work leads to multiple research developments. The results here obtained can be replicated for longer datasets. The non-aggregated data can be represented in an even more disaggregated form (firm level). Finally, the use of the micro data, one of the major contributions of this thesis, can be useful in the imputation of missing values and the creation of flash estimates of macroeconomic indicator (nowcasting).

Internet search engines produce large amounts of data. This thesis shows how the data about internet searches can be used for inflation forecasting. The internet search data is constructed from searches performed on Google. The sample covers eurozone countries over the period from January 2004 to July 2017. The performance of the internet searches is evaluated relative to traditional inflation forecasting benchmark models. The usefulness of the Google searches is evaluated by Granger causality and out-of-sample performance. Furthermore, to study the robustness of the results, the out-of-sample forecasting accuracy has been evaluated in two separate sub-samples. In this study, a simple autoregressive model augmented with internet searches is found to outperform the traditional benchmark models in predicting the month-over-month inflation of the near future. Moreover, the improvement is statistically significant in one-month ahead forecasting accuracy. The Google model also outperforms the benchmark models in year-over-year inflation forecasting. However, the improvement in year-over-year forecasting accuracy is modest. In addition, this thesis shows that the seasonally adjusted internet search data can improve the performance of the Google model slightly. This thesis is related to fast-growing research on employing Google Trends data in economic forecasting. The findings in this thesis require further research in exploiting the internet search data in macroeconomic forecasting.

The science of fish stock assessment is one that is very resource and labor intensive, with stock assessment models historically being based on data that causes a model to overestimate the strength of a population, sometimes with drastic consequences. The need of cost-effective assessment models and approaches increases, which is why I looked into using Bayesian modeling and networks as an approach not often used in fisheries science. I wanted to determine if it could be used to predict both recruitment and spawning stock biomass of four fish species in the north Atlantic, cod, haddock, pollock and capelin, based on no other evidence other than the recruitment or biomass data of the other species and if these results could be used to lower the uncertanties of fish stock models. I used data available on the RAM legacy database to produce four different models with the statistical software R, based on four different Bayes algorithms found in the R-package bnlearn, two based on continuous data and two based on discrete data. What I found was that there is much potential in the Bayesian approach to stock prediction and forecasting, as our prediction error percentage ranged between 1 and 40 percent. The best predictions were made when the species used as evidence had a high correlation coefficient with the target species, which was the case with cod and haddock biomass, which had a unusually high correlation of 0.96. As such, this approach could be used to make preliminary models of interactions between a high amount of species in a specific area, where there is data abundantly available and these models could be used to lower the uncertanties of the stock assessments. However, more research into the applicability for this approach to other species and areas needs to be conducted.

The history of tractor in Finland is 100 years old and in the whole world 120 years old. Development of tractors is continually ongoing. During the first decades it concentrated on engines. The introduction of air-filled tyres made it possible to increase speed on the road, which in turn lead to an increase in the number of gears. Most of the inventions within transmissions were made during the 1950s. The first powershift gears, stepless hydrostatic transmissions, fuell-sell tractor, range-gear and the power shuttle were all introduced during this time. Over the next decades these features were improved and presented as new inventions. The hydrostatic-mechanical power split continuously variable transmission (CVT) has become more common in recent years, but the basic invention was already in use elsewhere during the 1910s. The first CVT tractor was the Fendt 926, which was launched in 1995. Later introductions came in 1999, when ZF’s Eccom and the S-Matic both came to the market. Of all the CVT tractors that were introduced to the market up until 2008, only the John Deere IVT ant the Valtra Direct machines were equipped with the manufacturer’s own diesel engines and CVT transmissions. All other CVT tractors were manufactured using five different transmissions and engines. In the coming years, several more transmissions and brands will appear on the market. Mechanical Torotraks and steel belt variators will be available for low-horsepower tractors in the sub-75 kW class. At the same time the number of brands seen in the CVT arena is increasing and the differences in the construction of stepless transmissions will grow. Current CVT transmissions differ from each greatly, with different functional principles, functionality and structure. Transmissions are divided into two main categories on the basis on functional principle, either summing up torque or summing up speed. The functionality division in mostly based on the hydrostatic part. In a full-CVT transmission, the percentage decrease of hydrostatic transmission has s linear relationship with the percentage increase in running speed. The function of the hydrostat in a semi-CVT transmission in to balance the speed differences between different gearing rations. In these transmissions the hydrostatic part of the transmission in around 20–40%. The percentage of hydrostatic transmission in double-CVT transmission varies with the driving speed. Double-CVT transmissions can have several driving speeds where the percentage of mechanical transmission is very close to 100%. The theoretical predictions about how common new features will become is based upon a study of four-wheel tractors in Western Europe and Finland. This can be precisely calculated using Logistic-funktion the result would be better if the source data covered a longer time period. The regular S-curve depicts how common the new features will become in tractors of the future. The real growth area is during the period when the market share of 4wd tractors increase from 10 to 90 %. This shows that the annual growth in Western Europe was 4,0% and in Finland 7,5%. Within the next few years it will become necessary to study further new entrants of the CVT transmission market and to make predictions more precise by means of increasing the amount of source data. The users driving with conventional transmissions could utilise the driving strategies of CVT transmissions. Tractor manufacturers should ensure that their customers are fully educated in the use of their new machines, in order that they develop the correct driving habits. This in an important part of postmarketing strategy.