Both wind and solar monthly anomalies were found to show some correlation with the climate modes tested. To the extent that these climate modes are persistent or dynamically predictable, long-range forecasts of these anomalies are possible. Statistical and dynamical methods can be used to predict sea surface temperatures [30,57,58], which are strongly associated with many of the climate modes, and there are also other sources of seasonal predictability, for example related to snow cover and soil moisture [59,60]. Thus, improvements in NAO forecasting have led to better winter wind power forecasts over Europe .
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It is somewhat surprising that such a simple statistical model could have fared so well in predicting the interannual variations of the Antarctic sea ice field, a task that has rarely been attempted before. The predictability demonstrated here can be attributed to the domination of the coupled air-sea-ice system by a few distinctive, slowly-changing modes such as the Antarctic dipole, although a self-sustained low-frequency oscillation is not likely to exist in Antarctic. Our understanding of the physical processes operating in the Antarctic climate system is still rather limited, but we do have a good grasp of the statistical characteristics of the system, and a well- constructed statistical model can be a useful forecast tool as long as it contains those dominant climate modes. This self-evolving Markov model emphasizes the regional air-sea-ice interaction, and as such it does not explicitly simulate the Antarctic-low-latitude teleconnection. However, the success of the model does not compromise in any way the importance of the teleconnection. Since the Antarctic interannual disturbances are likely to be excited in the first place by the influence from low latitudes, as evident in their lagged response to ENSO, any models that simulate the evolution of these disturbances implicitly take into account the teleconnection. What the model does is nothing but to pick out significant signals from observed initial conditions and predict a statistically meaningful path for the movement, growth, or decay of these initial disturbances.
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The ensemble hindcast and real-time forecast datasets in- cluding the monthly specific humidity and wind field at dif- ferent levels and monthly precipitation from Climate Fore- cast System version 2 (CFSv2) (Saha et al., 2014) were used here to quantify the potential predictability. The predicted moisture flux was calculated in the same way as the obser- vation mentioned in Sect. 2.1. CFSv2 has 24 ensemble mem- bers with different initial conditions (Yuan et al., 2011) and has been widely used for subseasonal to seasonal forecast- ing (e.g., Kirtman et al., 2014; Yuan et al., 2015; Tian et al., 2017). All monthly anomalies were calculated based on the climatology from the entire hindcast period (1982–2010). The 0.5-month lead forecast ensembles started from mid- May to early June (Saha et al., 2014) and predicted through June–July. Similarly, the 1.5-month lead forecasts for June– July started from the middle of April, and so on.
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Abstract. The importance of initial state and boundary forc- ing for atmospheric predictability is explored on global to regional spatial scales and on daily to seasonal time scales. A general circulation model is used to conduct predictabil- ity experiments with different combinations of initial and boundary conditions. The experiments are verified under perfect model assumptions as well as against observational data. From initial conditions alone, there is significant in- stantaneous forecast skill out to 2 months. Different initial conditions show different predictability using the same kind of boundary forcing. Even on seasonal time scales, using observed atmospheric initial conditions leads to a substan- tial increase in overall skill, especially during periods with weak tropical forcing. The impact of boundary forcing on predictability is detectable after 10 days and leads to mea- surable instantaneous forecast skill at very long lead times. Over the Northern Hemisphere, it takes roughly 4 weeks for boundary conditions to reach the same effect on predictabil- ity as initial conditions. During events with strong tropical forcing, these time scales are somewhat shorter. Over the Southern Hemisphere, there is a strongly enhanced influence of initial conditions during summer. We conclude that the long term memory of initial conditions is important for sea- sonal forecasting.
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The simulated temperature at 850 hPa and geopotential height at 500 hPa of individual ensemble members are com- pared against the relevant ECMWF operational analyses in order to evaluate the predictability of the event. Some mem- bers indicate an early warning of the event and reveal the large-scale spatiotemporal characteristics of the blocking system that prevailed over Russia even 3 months in advance. For instance, the member initialized at 22 April 2010 and referenced as 0422 satisfactorily reproduced the main block- ing pattern over eastern Europe for 16 July at 12:00 UTC (Fig. 7a). However this member simulated a northward ex- tended and more intense system compared to an early staged blocking system depicted in the ECMWF analysis (Fig. 7b). Four days later the 0422 member displaced a mature stage system over central Russia while in the ECMWF analysis the blocking pattern was still in developing stages over eastern Europe (Fig. 8a, b). Despite the fact of the early warning this member missed the phase of the system and its spatiotem- poral characteristics as well, predicting a short-lived east- ward propagating blocking pattern. The individual member initialized at 25 April 2010 and referenced as 0425 further improved the prediction of the blocking system on 16 July at 12:00 UTC (Fig. 9a) reproducing a less northward-extended
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Researchers developing new forecast products should con- sider several of the predictor variables discussed here. Fore- casts of the frequency of extreme rainfall events would likely provide a better indication of floodiness, compared to seasonal total rainfall forecasts, for much of Sub-Saharan Africa. Studies have shown the potential predictability of this variable in several locations (Anderson et al., 2015; Higgins et al., 2000; Verbist et al., 2010). Seasonal forecasts of soil moisture could give a useful indication of flood risk in dry regions of Africa (Fig. 4), and these forecasts are also likely to have seasonal predictability in areas where they can be well initialized, notably due to the persistence of soil mois- ture (Kanamitsu et al., 2002; Koster et al., 2010; Poveda et al., 2001). This also takes evaporation into account.
In fact, NMME climate predictions have lower predictabil- ity and forecast skill in the northwest inland areas of China, in comparison with southeast monsoon regions (Ma et al., 2016). The HRB is located far from oceans and in mid- latitudes, and is little affected by SST from oceans, espe- cially equatorial oceans, which are the major source of pre- dictability at a seasonal timescale. Topographic influence on regional and local weather and climate cannot be resolved by GCMs, for example, local ascending motion affected by the Qinghai–Tibet Plateau exists and has a considerable impact on precipitation over the HRB (Sun et al., 2011). In addition, the joint extreme phases of climate oscillations instead of a single one could trigger extremes (e.g., drought) over the arid endorheic basin, and almost no climate models can capture the complex and multiple teleconnections (Shi et al., 2016).
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Consideration of the limits of human mobility predictability and the provision of a mathematical approach to computer an upper bound for a given dataset was first undertaken in . Considering a geo-locational dataset obtained from cell tower logs, heuristically corrected to conform to a 1hr sampling rate, the authors were able to provide an insight into the upper bound on predictability, with a value of 93% reported. Using their formula a number of researches have since interrogated different datasets – in order either extend or address some perceived weaknesses of the approach.  replicated the results of  using 14 participants via a combination of sensors while also investigating the effect of varying temporal resolution. They demonstrated that the upper limit of pre- dictability increases as the temporal resolution becomes finer- grained. Later, using the Geolife dataset  and  noted a direct relationship between spatial quantization and the upper bound on predictability [3, p388-389]. Most recently,  again considered varying spatiotemporal quantizations but in contrast to  used a dataset also including indoor locations. However, compared to all previous studies this produced significantly higher upper bounds (although as their dataset is not publicly available it is not possible to replicate these results). We also note that some works such as – have considered the performance of pre-existing prediction algorithms. However, because we focus on the theoretical bounds of an optimal pre- dictor rather than current algorithms their work is considered complementary rather than directly related.
In this article, a method explicitly based on a simple measure of tempo- ral predictability is introduced. The main contribution of this article is to demonstrate that maximizing temporal predictability alone can be sufficient for separating signal sources. No formal proof is given of the temporal pre- dictability conjecture stated in previously in this section, the main purpose being to demonstrate the utility associated with this conjecture. Although counterexamples to this informal conjecture are easy to construct, a formal definition of the conjecture is robust with respect to such counterexamples (see section 1.4). Moreover, results presented here suggest that the conjecture holds true for many physically realistic signals, such as voices and music.
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The predictability of a particular flood type can provide crucial information for the decision makers which include the water management and flood emergency commissions responsible for the measures possibly mitigating the flood damage, such as the discharge control of the reservoirs, the construction of the flood walls or deploying sand bags, etc. According to the experience of the authors, the users tend to either doubt the forecast or to trust the forecast output uncriti- cally. Therefore, it is recommended to supple- ment the hydrological forecast with a qualitative explanation which outlines the limitations of the particular forecast that should be also considered as “what-if” scenario.
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Here, we lay out the documented patterns in return predictability that are at odds with the efficient markets hypothesis and potentially attributable to overconfidence. We first concen- trate primarily on the nature and direction of the patterns, as opposed to their magnitudes. Of course, it is possible that the abnormal returns generated by “anomaly portfolios” based on patterns of predictable returns are not anomalous at all. A strategy may earn high returns relative to some benchmark by virtue of exposure to some systematic risk factor that the benchmark does not capture. (A factor in the asset pricing literature refers to a statistical source of common variation in security returns—usually the return on a portfolio. For ex- ample, the returns of individual stocks can be explained in part by realizations of the stock market as a whole, as is verified by regressing stock returns on the market portfolio.) In the next subsection, we will argue that the large premia earned by a combination of these anomaly-based strategies is too large to be explained plausibly in this way. We consider evidence on return predictability of three types: (1) predictability based on the market price of the firm, scaled by measures of fundamental value; (2) predictability based on a recent his- tory of past returns (momentum and reversal); and (3) predictability based on underreaction to, or neglect of, public information about fundamentals.
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Figure 3 illustrates the variance ratio profile of the 14 selected models with predictability potential, and presents the area in which the VRP of random walks generated by the Monte Carlo simulation are concentrated, corresponding to the grey area of the graph. This area was defined as the interval containing from 1% to 99% of the simulated distribution. As we may observe, the VRP of the models are concentrated below the random walks region, indicating that the models show a reversion to the mean. Some models may be found outside the region, since, in addition to its position, the Mahalanobis distance takes the entire format of the curve into consideration. In this way, a curve with a format different from the average of random walks may show a sufficiently large Dm to be considered as non-random.
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Finally, we should mention that a few papers have looked at the international dimen- sion of dividend-growth predictability before us. For instance, in his survey, Campbell (2003) reports dividend growth rate predictability for a few developed countries but not for the U.S. equity market. Ang and Bekaert (2007) look at the U.S., the U.K., France, and Germany, i.e. large equity markets, and conclude that “[...] the evidence for linear cash-flow predictability by the dividend yield is weak and not robust across countries or sample periods” (p. 670). A recent paper by Engsted and Pedersen (2010) investigates long time series for four countries (U.S., U.K., Denmark, and Sweden) and shows that dividend yields do not predict dividend growth rates in the U.K. and U.S. (i.e., large countries), but do so in Denmark and Sweden (i.e., small countries). In relation to Campbell (2003), Ang and Bekaert (2007), and Engsted and Pedersen (2010), we provide evidence for many more countries, which allows us to verify systematic differences in predictability patterns across countries. We also link dividend predictability across the globe to cross-country differences in firm characteristics (such as firm size and cash-flow volatility) and dividend smoothing to shed light on the mechanism driving dividend predictability by the dividend yield.
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In the third Chapter of the thesis, we have exploited the peculiarities of commodity markets to show that fundamental news about global growth is reflected into prices, but not instantaneously. The news can, therefore, be filtered in real-time from commodity prices, but such news takes several months before being fully incorporated into prices, leading to returns predictability. Coherently with the theories of overreaction and underreaction to news, we have shown using simulated data that the results obtained can be explained by the existence of latecomers, who process information with a delay, and momentum traders. Our analysis has assumed therefore that not all investors have access to the same information and learn from past experience. We are confident that such assumptions, not only allow to give a simple explanation to the existence of asset pricing anomalies such as prices under and overreaction to news and the time-series momentum but also provide a more realistic representation of investors’ behaviour. Asymmetric information, high costs of data analysis, unstable time-series relationships and limited data samples, often force investors to use simpler expectations formation rules that are far from being perfectly rational and should be taken into account more seriously in future research.
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as speculated by Schar et al. (2004) while demonstrating the predictability of the central Asian river flow. Overall the above results indicate that the IMD rainfall and temperature data along with station snow information is able to credibly represent the variations of the spring seasonal Satluj River flow, while it probably misses smaller-scale features associ- ated with the complex structure of the topography. An anal- ysis was also undertaken to look at the ability of precipi- tation data from local meteorological stations to represent observed interannual variability of streamflow of the Satluj River, which was satisfactorily skillful. This is consistent with Schar et al. (2004) who also demonstrated that precipi- tation data in winter alone is sufficient to skillfully predict the next seasonal river flow in central Asia. Despite the low res- olution of IMD precipitation data and poor coverage of sta- tion meteorological data, the results appeared highly promis- ing. In most years the models provided excellent predictions on 1 March and 1 April, and only a few years (includes 2004) had a departure from the prediction interval (based on a p = 95 % prediction interval, one would expect at least one miss in a 15 yr series, according to Schar et al. (2004)). The 1 March and 1 April models also enabled forecasts that rep- resent an improvement in comparison with the climatological forecast.
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The data of current study were analyzed through SPSS 18 version. Alpha reliability were computed in order to ensure psychometric strength of scales and sub-scales used in study; correlation matrix was computed of all variables to have an insight in relationship pattern and finally regression analysis was used for hypothesis testing. Table 1 represents the correlation matrix, means, and standard deviations computed for the sample of both dual and single career couples for all pairs of scores for total marital satisfaction, trust and its three subscales. Table 1 also tells us that except predictability which is a subscale of trust all the variable ware significantly and positively correlated with marital satisfaction.
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Because we want to see whether the return is random or not, we calculate the daily returns from the daily data, the weekly returns from the weekly data for the Shanghai Composite Index. The entire set is divided into a three separate data sets for different usage. The first data is called the “Training” data set and is used for training and adjusting the coefficients or weights of the systems. The second is the ‘Verification’ data set which is used for verifying the predictive performance of the trained sys- tems and evaluating the choice of parameters for a good trading system. Finally the third data set or ‘Test’ data set is used for an actual trading test to determine the trading performance of the chosen trading system. We set the training data from 2000 to 2006, the verification data from 2007 to 2008 and the test data from 2009 to 2010. 3.1.3. Predictability Experiments of Shanghai
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Following a line of research that incorporates the information content of a word as well as collo- cation measures (Pan and McKeown, 1999; Pan and Hirschberg, 2001) we have included a number of probabilistic variables. The probabilistic vari- ables we used were the unigram frequency, the pre- dictability of a word given the preceding word (bi- gram), the predictability of a word given the follow- ing word (reverse bigram), the joint probability of a word with the preceding (joint), and the joint prob- ability of a word with the following word (reverse joint). Table 2 provides the definition for these, as well as high probability examples from the cor- pus (the emphasized word being the current target). Note all probabilistic variables were in log scale.
of sea ice extent predictability, the start dates were binned by sea ice extent and, to assess the dependence of volume predictability, they were binned by volume. The ACC and NRMSE were recalculated for each of these bins (see Fig. 6). According to Fig. 6, whether the predictability changes with the distance of the initial state from the mean extent and volume appears to depend on the metric. For states initialised close to the mean sea ice volume climatology, the ACC met- ric decreases much more rapidly with lead time than the high or low cases, appearing to recover towards the end of the simulations. Indeed, the multi-model mean ACC falls dra- matically in the medium case compared to the low and high years. However, similar features are not present when using the NRMSE metric, with the mean NRMSE increasing with lead time at a similar rate across the high, medium and low cases. We therefore believe that this behaviour is a statisti- cal artefact of the ACC metric, for the following reason. For start dates initialised close to climatology, the numerator of the ACC metric (Eq. 2) will fluctuate between positive and negative values as the ensemble members diverge, more fre- quently than when initialised from a large anomaly. When started from a large anomaly, the ensemble members will agree more strongly on the sign. This leads to lower ACC in the medium cases. Similar behaviour is observed when ex- periments are binned by high, low and medium initial sea ice extent (not shown). With so few data points it is not possible
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DOI: 10.4236/acs.2019.94038 618 Atmospheric and Climate Sciences is characterized by droughts in Equatorial Eastern Africa and flooding in South- ern Africa. This regional scale climate feature can be recognized as a seesaw or dipole pattern. Additional discussions of the ENSO teleconnection with seasonal precipitation over the region may be found in     among other studies and authors. It is useful to determine how well the superensemble pre- dicts this regional feature. In Eastern Africa, seasonal forecasts of climate are usually done SSTs and ENSO derived statistical relationships with precipitation. The statistical models cannot always give forecasts that are physically consistent with the large-scale mechanisms such as the atmospheric circulation characteris- tics and its modulation by regional and local scale processes. In this study, it is sufficient to focus on the skill of the SSE relative to the EM in forecasting this see-saw pattern in precipitation between Tropical East Africa and Southern Parts the larger Eastern Africa. By obtaining the average of the warm ENSO and cold ENSO events within the model data sets used in this study, Figure 11 summa- rizes the predictability of the regional precipitation gradient in which panels (a) and (d) show observations for the gradient pattern, panels (b) and (e) illustrate predictability by the SSE scheme and panels (c) and (f) show the performance of the EM. For the two scenarios, it is found that the multimodel superensemble simulates the “see-saw” in precipitation quite consistently with observations and outperforms the ensemble mean in getting accurately the magnitudes and spatial distribution of the season precipitation.
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