Background Forecasts are used as a basis for decision making and they mainly affect decisions at strategic and tactical levels in a company or organization. There are two different methods to perform forecasts. The first one is a qualitative method where a n expert or group of experts tell about the future. The second one is a quantitative method where forecast are produced by mathematical and statistical models. This study used a quantitative method to build a forecast model and took into account external f actors in forecasting the sales volume of Bosch Rexroth’s hydraulic motors. There is a very wide range of external factors and only a limited selection had been analyzed in this study. The selection of the variables was based on the markets where Bosch Rexroth products are used, such as mining. Purpose This study aimed to develop five predictive models: one model for the global sales volume, one model each for sales volume in USA and China and one model each for sales volume of CA engine and Viking engine. By identifying external factors that showed significant relationship in various time lags with Bosch Rexroth’s sales volume, the forecasts 2016 and 2017 were produced. Methods The study used a combination of multiple linear regression and a Box - Jenkins AR MA errors to analyze the association of external factors and to produce forecasts. Externa l factors such as commodity prices, inflation and exchange rates between different currencies were taken into account. By using a cross - correlation function between external factors and the sales volume, significant external factors in different time lags were identified and then put into the model. The forecasting method used is a Causal forecasting model. Conclusions The global sales volume of Bosch Rexroth turned out to be affected by the historical price of copper in three different time lags , one, six and seven months . From 2010 to 2015, the copper price have been continuously dropping which explain s the downward trend of the sales volume. The sales volume in The U SA showed a significant association by the price of coal with three and four time lags. This means that the change of coal price takes three and four months before it affects the sales volume in the USA. The market in China showed to be affected by the development of the price of silver. The volume of sales is affected by the price of silver by four and six time lags. CA engine also displayed association with the price of copper at the same time lags as in the global sales volume. On the other hand, Viking engine showed no significant association at all with any of the external factors that were analyzed in this study. The forecast for global mean sales volume will be between 253 to 309 units a month for May 2016 – December 2017. Mean sales volume in USA projected to be in between 24 to 32 units per month. China's mean sales volume is expected to be in between 42 to 81 units a month. Mean sales volume of CA engine has a forecast of 175 to 212 units a month. While the mean s ales of Viking engine projected to stay in a constant volume of 25 units per month.

Bakgrund: Denna uppsats riktar in sig på hotell i Stockholm och all data som anges gäller för staden som helhet. Inom Hotellbranschen finns det tre vedertagna nyckeltal som kan sägas beskriva hur det går ekonomiskt för ett hotell. Då hotellen till stor del styrs efter dessa tre nyckeltal så är det av stort intresse för varje enskilt hotell att jämföra sina egna värden med marknadens värden på dessa nyckeltal. Om prognoser utförs på dessa nyckeltal borde det vara av stort intresse för varje hotell att ta del av dessa prognoser för att på så vis kunna reglera prissättningen utefter hur marknaden kommer att se ut den närmaste tiden. Syfte: Ta fram modeller som utifrån framtida evenemang och framtida bokningsläge prognostiserar hotellmarknadens Beläggning och Rumsintäkter. Utifrån dessa prognoser beräknas nyckeltalen Beläggning, Snittpris och intäkt per disponibelt rum på dagsnivå ett år fram i tiden, det vill säga för år 2016. Metod: Då datamaterialet består av tidsserier med tillhörande förklarande variabler används en typ av dynamisk regressionsmodell. Dessa modeller är utformade för att hantera tidsseriedata med tillhörande förklarande variabler. Modellen som används kallas för regression med ARMA-fel och syftar till att en multipel regression anpassas och en lämplig ARMA-modell tas fram för att förklara feltermerna. På så vis modelleras även autokorrelationen som annars finns kvar i feltermerna. Resultat: Modellen för Beläggningen består av fyra förklarande variabler och feltermerna antas följa en AR-struktur. Rumsintäkterna prognostiseras med en modell med sju förklarande variabler, även för denna modell antas feltermerna följa en AR-struktur. Det tycks också finnas en säsong i data vilken också modelleras i form av en AR-struktur för de båda modellerna. Prognosen för nyckeltalen ser till största del ut att följa föregående års mönster, och evenemangs-typen Event ger oftast en hög skattning i förhållande till månaden. Evenemangstypen Högtid tycks ge en negativ effekt och Bokningsläget har en positiv effekt för båda modellerna. Slutsats: Modellerna anses välanpassade men det krävs mer bearbetning på de förklarande variablerna där till exempel event bör grupperas in beroende på vad för slags event det är. För att prognostisera rumsintäkter bör en variabel som förklara hotellens prisjusteringar modelleras. / Background: This thesis targets hotels in Stockholm with aggregated data for the city. In the hotel market there’s three key indicators of particular interest and can be said describes how the market goes. Because of how much influence these key indicator have on the hotels it’s in great interest for the hotels to compare themselves with the market values. If these key indicators where forecasted it would perhaps be of great interest for the hotels to buy these forecasts to be able to control the room pricing in advance. Purpose: Develop forecasting models due to future event and bookings with occupancy and room revenue as response variables. The key indicators revenue per available rooms and average price is then calculated through these forecasts for the year 2016. Method: Since data consist of response variables (called output series) where the future values this series depends on past values of this series and a multiple set of related time series and external events (called input series) a dynamic regression called “regression with ARMA errors” where used. The method implies that you suit a multiple regression where the error terms are modelled with an appropriate ARMA model. Results: The model for occupancy consist of four dependent variables and the model for the room revenue contain seven dependent variables. The error terms for these models include an autoregressive model with both seasonal and non-seasonal orders. The forecast for the key indicators seems to follow the same pattern as previous years, where the event type Event more often than not gives a high estimate in relation to the current month. The event type Holiday seems to have a negative impact and bookings has a small positive effect for both models. Conclusions: The models seems to fit data well but the input series needs more processing where the variable event seems to need some subgrouping. To forecast the room revenue is seems like a variable explaining price changes need to be constructed.

En damm används i huvudsak för att magasinera vatten i energiutvinningssyfte. Dammen rör sig fram och tillbaka i ett säsongsmönster mestadels beroende på skillnader i utomhustemperatur och vattentemperaturen i magasinet. Det nordiska klimatet innebär risk för isläggning i magasinet, för vilken lasten är relativt outforskad. Denna rapport syftar till ett med multipla linjära regressionsmodeller samt dynamiska regressionsmodeller avgöra vilka variabler som förklarar en specifik svensk dammkonstruktions rörelse. Dammens rörelse mäts genom att mäta dammens förflyttning kontra berggrunden med data från dammens inverterade pendlar. Av särskilt intresse är att avgöra islastens påverkan på rörelsen. Resultaten visar att multipla linjära regressions-modeller inte fullständigt lyckas modellera dammens rörelse, då de har problem med autokorrelerade residualer. Detta hanteras med hjälp av autoregressiva regressionsmodeller där de initiala förklarande variablerna inkluderas, kallat dynamisk regression. Denna rapports resultat visar att de autoregressiva parametrarna fungerar mycket väl för att förklara pendlarna, men att även tid, temperatur, det hydrostatiska trycket samt istjocklek är användbara förklarande variabler. Istjockleken visar signifikant påverkan på 5 % signifikansnivå på två av de undersökta pendlarna, vilket är ett noterbart resultat. Författarna menar att rapportens resultat indikerar att det finns anledning att fortsätta forska kring islastens påverkan på dammkonstruktioner. / A dam is a structure mainly used for storing water and generating electricity. The structure of a dam moves in a season-based pattern, mainly because of the difference in temperature between the air on outside of the dam and the water on the inside. Due to the Nordic climate, occurrences of icing on the water in the basin is fairly frequent. The effects of ice on the structural load of the dam are relatively unexplored and are the subject to this bachelor’s thesis. The goal of this project is to evaluate which predictors are significant to the movement of the dam with multiple linear regression models and dynamic regressions. The movement is measured by inverted pendulums that register the dam’s movement compared to the foundation. It is of particular interest to determine if the ice load influences the movement of the dam. The multiple regression models used to explain the dam’s movement were all discarded due to autocorrelation in the residuals. This falsifies the models, since autocorrelation means that they don’t meet the needed assumptions. To counteract the autocorrelation, dynamic models with autoregressive terms were fitted. These models showed no problem with autocorrelation. The result from the dynamic models were successful and managed to significantly explain the movement of the dam. The autoregressive terms proved to be efficient explanatory variables. The dynamic regression models also show that the time, temperature, hydrostatic pressure and ice thickness variables are also useful explanatory variables. The ice thickness shows a significant effect at the 5 % significance level on two of the investigated pendulums. The report's results indicate that there is reason to continue research on the ice load impact on dam constructions.